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In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products' biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method's reliability, security, and efficiency. The experimental results show that this research scheme's initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry. [ABSTRACT FROM AUTHOR]

Security of modern classical data encryption often relies on computationally hard problems, which can be trivialized with the advent of quantum computers. A potential remedy for this is quantum communication which takes advantage of the laws of quantum physics to provide secure exchange of information. Here, quantum key distribution (QKD) represents a powerful tool, allowing for unconditionally secure quantum communication between remote parties. At the same time, microwave quantum communication is set to play an important role in future quantum networks because of its natural frequency compatibility with superconducting quantum processors and modern near-distance communication standards. To this end, we present an experimental realization of a continuous-variable QKD protocol based on propagating displaced squeezed microwave states. We use superconducting parametric devices for generation and single-shot quadrature detection of these states. We demonstrate unconditional security in our experimental microwave QKD setting. The security performance is shown to be improved by adding finite trusted noise on the preparation side. Our results indicate feasibility of secure microwave quantum communication with the currently available technology in both open-air (up to ~ 80 m) and cryogenic (over 1000 m) conditions. Microwave-based quantum key distribution would allow a secure exchange of information in superconducting local area networks and, potentially beyond, in open-air conditions. Here, the authors provide a proof-of-principle implementation within a cryogenic environment, based on propagating displaced squeezed microwave states. [ABSTRACT FROM AUTHOR]

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security. [ABSTRACT FROM AUTHOR]

Medical records are transmitted between medical institutions using cloud-based Electronic health record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic constrained message authentication (DCMA) technique is used in the proposed system to encrypt the outsourced medical data by using symmetric key cryptography, which uses the Seagull optimization algorithm (SOA) to choose the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The results of the proposed model are evaluated using performance metrics, and the model attained a security of about 98.58%, which is proven to be superior because it adopts advanced random secret key generation, which adds more security to the system. [ABSTRACT FROM AUTHOR]

Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir's Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS. [ABSTRACT FROM AUTHOR]

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner. Homomorphic encryption contributes to eliminating security issues during the data communication for edge computing. Here, the authors propose a 1-trasistor−1-memristor array as a homomorphic encryption hardware prototype. By leveraging the stochastic set-switching behavior and analog non-volatile properties of the memristor, the complete homomorphic communication process is demonstrated. [ABSTRACT FROM AUTHOR]

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower–upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NC wat 1 / NC wat 2 of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2 100 , which provides a high protection degree against brute-force attacks. The proposed scheme achieves better robustness, imperceptibility, and security performance than previous schemes. Our method can reduce time costs by 98% (91% on average), which results in lower time costs than other conventional encryption techniques. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are an essential part of several aspects of daily life for people. They are utilized in a variety of contexts, including industrial monitoring, environmental sensing, and so on. But, secure communication is the major challenge in the IoT environment. Therefore, a decentralized Blockchain-based Key Management protocol using Levy Flight-Equilibrium Optimization and Self-Attention-based Improved Faster Region-based Convolutional Neural Network (BlkKM) method is proposed to determine stable security in tamper-resistant hardware machine that can protect sensitive secret data in the healthcare field i.e., stored cryptographic keys. The keys are categorized as Key Encryption Keys (KEKs) and Data Encryption Keys (DEKs). The number of the keys is decreased by using Levy Flight- Equilibrium Optimization (LF-EO) as organizing nodes with logical sets. Also, Self-Attention-based Improved Faster Region-based Convolutional Neural Network (SA-based IFRCNN) is used for reordering a set of logical nodes to minimize the number of sets after a node exits the network. Additionally, the system makes use of smart contracts for access control as well as proxy encryption to data encryption. The proposed method is compared with existing techniques to validate the security enhancement performance. The evaluation is performed based on throughput, end-to-end delay, storage overheads, and energy consumption. The experimentation results revealed that the proposed method improved the throughput to 220.52bps and diminished the utilization of energy. A greater degree of memory usage is also decreased by using this technique. [ABSTRACT FROM AUTHOR]

Crowdsourcing has long been an effective way for data collection. Despite numerous research efforts devoted to crowdsourcing security, most crowdsourcing platforms in practice are still storing and processing plaintext data, which is vulnerable to in-house attacks. To fill this gap, we envision a full-fledged crowdsourcing platform with end-to-end encryption (E2EE), where the crowdsourced data remains always encrypted to the platform. Such a design would serve as an in-depth defense strategy against data breaches from both internal and external threats and provide technical means for crowdsourcing service providers to meet various stringent regulatory compliance. To realize this vision, we will discuss the technical requirements and related challenges, including: (1) securing worker profiles in task matching to respect individual privacy, (2) assuring high-quality crowdsourced data to enhance data values, (3) enabling versatile data analytics to uncover data insights, (4) protecting data at the front-end to fully achieve E2EE, and (5) preventing the abuse of E2EE for practical deployment. We will overview the limitations of prior arts in meeting all these requirements and identify potential research directions for the roadmap ahead. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

We show that the scheme (Telecommun Syst 78:539–557, 2021) is flawed. It uses a symmetric key encryption to transfer data between vehicles and the grid. But the specified symmetric key is easily retrieved by an adversary, which results in the loss of data confidentiality, and makes it vulnerable to impersonation attack, man-in-the-middle attack, and replay attack. We also present a method to fix this flaw and remove some superfluous communications in its registration phase. [ABSTRACT FROM AUTHOR]

Chatbot is an artificial intelligence application that can provide a conversational environment between humans and machines. Most organizations and industries are willing to lay out their services through chatbots because they can provide 24/7 customer support. Meanwhile, it raises security and privacy challenges like access control, data leakage during transmission, SQL injection attacks, and language model attacks, which make the users concerned about their data, performance, and accuracy. Therefore, this research paper proposes a comprehensive framework integrating blockchain, federated learning, and a fully homomorphic encryption algorithm with face recognition to solve the above-mentioned chatbot's challenges. The experimental result shows that a distributed system improves chatbot accuracy (90%) and that more transactions in less time with more clients do not affect the performance. In contrast, more iterations and clients will decrease the accuracy, performance, and transactions in a centralized system. In addition, fully homomorphic encryption improves and speeds up the data encryption process. It encrypted more data (1792 MB) in a small amount of 1240 times per second, and conversations and transactions can be transferred via a secure network to ensure the confidentiality, integrity, and authenticity of users' data. The implementation of such a comprehensive framework in real-life situations can improve chatbot security when it actively works as a customer agent in an organization. [ABSTRACT FROM AUTHOR]

Authenticated encryption with associated data (AEAD) has become prominent over time because it offers authenticity and confidentiality simultaneously. In 2018, the National Institute of Standards and Technology (NIST) initiated a competition to standardize lightweight AEAD and hash functions, with Ascon as the final winner among the 10 finalists. Numerous prior works evaluated their performance on FPGA and ASIC, but not on a parallel architecture like GPU, which is a common accelerator already found in many existing cloud servers. In this work, the first GPU implementation of the NIST AEAD finalists is proposed. Several GPU implementation techniques applicable to all AEAD schemes are presented, along with novel techniques for some specific schemes to enhance throughput performance. Experimental results show that all NIST AEAD finalists can achieve high throughput (up to 111.53M AEAD per second), approximately 142.19% and 72.65% improvement compared to unoptimized GPU version, and the investigated FPGA results respectively. [ABSTRACT FROM AUTHOR]

Verifiable outsourced attribute-based encryption (VO-ABE) enables one-to-many data sharing and fine-grained access control under lower trust, making it suitable for cloud or edge systems involving resource-constrained devices. There is no fully outsourced and fully verifiable attribute-based encryption scheme or key-policy VO-ABE scheme. Moreover, the previous VO-ABE schemes require multiple rounds of interaction or high verification costs to support verifiable outsourced key generation and verifiable outsourced encryption. To address these issues, in this paper, we propose an effective key-policy fully outsourced and fully verifiable attribute-based encryption scheme supporting verifiable outsourced key generation, encryption, and decryption simultaneously. We formally define two new properties: outsourced key generation verifiability and outsourced encryption verifiability. Analysis and simulation show that the proposed scheme performs well and is practical. All local computational overheads of the proposed scheme are constant and do not increase with the number of attributes or the complexity of access structures. [ABSTRACT FROM AUTHOR]

IoT devices produce a vast amount of data ranging from personal to sensitive information. Usually, these devices remain connected to the internet so protecting the information produced by them is crucial. Since most of the IoT devices are resource-constrained, they must be supported with lightweight encryption standards to protect information. Recent research has used the concept of context awareness to select the most suitable data encryption standard based on the device resources along with the required information confidentiality level. However, to effectively use the context information, it is required to be organized explicitly while considering the dynamic nature of IoT systems. In this regard, ontology-based systems effectively reduce the volume of manual work while recommending solutions. Currently, these systems cannot work with precision due to multiple uncertain factors of IoT sensory data. To overcome this challenge, this research proposes a fuzzy ontology-based context-aware system to protect IoT device information with the help of an encryption algorithm that considers device capabilities and user priorities regarding the data confidentiality. In order to automate the recommendation process, Semantic Web Rule Language (SWRL) rules and fuzzy logic are used, whereas, Description Logic and RDF Query Language is used to evaluate the results. The evaluation results confirm that the proposed method can produce results according to human perception by significantly increasing the accuracy of prediction. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of cloud storage, deduplication technology has been widely used. In personal cloud storage, files are diverse. Diverse files contain files of different sizes and different popularity, some of which have many copies. Existing deduplication schemes mainly focus on data security but fail to improve the overall performance, including low computing overhead for diverse data. Using deterministic tags based on convergent encryption to identify data may leak data information, while using fully random tags generated by complex encryption algorithms may generate more computing overhead. To address the above issues, we proposed an encrypted deduplication scheme based on files diversity (FD-Dedup). Diverse files are identified by semi-random tags. We also designed a semi-random tag generation (SRTG) algorithm that coordinates the computing overhead and security. Security analysis and performance comparison show that FD-Dedup can balance security and computing overhead for diverse files in personal cloud storage. [ABSTRACT FROM AUTHOR]

Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing in this paper. The proposed method achieves fne-grained access control with low computation consumption. We design Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption that reinforces attribute revocation and averts the discharge of sensitive information. Furthermore, we utilize Optimal Cache Oblivious algorithm to prevent disclosure of access patterns to hide the access patterns in cloud storage via rand pattern matching. We support updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. We perform secure data sharing to achieve higher data confidentiality and integrity. Finally, we conducted the extensive experiments in cloud and the results illustrated that our proposed BGNBA-OCO method is more efficient than related works. [ABSTRACT FROM AUTHOR]

Photoluminescence (PL) colour-changing materials in response to photostimulus play an increasingly significant role in intelligent applications for their programmability. Nevertheless, current research mainly focuses on photochemical processes, with less attention to PL transformation through uniform aggregation mode adjustment. Here we show photochromic luminescence in organic crystals (e.g. dimethyl terephthalate) with PL varying from dark blue to purple, then to bright orange-red, and finally to red. This change is attributed to the emergence of clusters with red emission, which is barely achieved in single-benzene-based structures, thanks to the subtle molecular rearrangements prompted by light. Crucial to this process are the through-space electron interactions among molecules and moderate short contacts between ester groups. The irradiated crystals exhibit reversible PL transformation upon sufficient relaxation, showing promising applications in information storage and smart optoelectronic devices. This research contributes to the development of smart photochromic luminescent materials with significant PL colour transformations through molecular rearrangement. Color-changing emissions in organic crystals are often realized via photochemical reactions of their molecules. Here, the authors demonstrate reversible color change through more simple molecular rearrangements with application in encryption and data storage. [ABSTRACT FROM AUTHOR]

One of the routine exercises to manage and improve the performance and utility of the cloud is the migration or transfer of cloud data whether it is large or small. However, it is extremely challenging to protect both data privacy and integrity concurrently while moving cloud data, particularly when it is very vast. Collectively, prior works fail to offer a complete solution to these problem. Even though data encryption and steganography techniques are popular and efficient, they provide higher time and space complexities and introduce information loss. As a result, the goal of this research is to provide a chaos compression and encryption system based on chaos theory to guarantee both data privacy and integrity during the transit or migration of massive cloud data. During data transmission, the entire data are compressed using a chaotic substitution box followed by an adaptive Huffman encoding algorithms. Therefore, the input data are efficiently transformed into a non-readable form which replaces the original data, making it difficult for an unethical individual or group to determine its true sense. Our evaluation results show that the proposed chaotic technique has a maximum entropy value of 7.99, which supports its ability to provide more privacy when compared to previous techniques. It also delivers the best bits per code of 4.41, a throughput of 2.89 MB/s, and a minimal information loss percentage of 0.0011%, demonstrating its superior time, space efficiency, and ability to improve data integrity over existing methods. [ABSTRACT FROM AUTHOR]

In recent years, Censorship and anti-censorship technology is developing rapidly. The censorship and surveillance systems track traffic on the network; they identify protocols and traffic, block malicious and illegal transportation, and keep the network under control. It can protect the security of network users but also bring risks to their private security, especially that of users chat. In this paper, we introduce MuChat, a free convert chat application for instant communication on mobile, which is designed as censorship as well as surveillance-resistant platform for coverting communication in the censorship region. To evade detection by Internet censors, MuChat uses multi-instant messaging channels provided by public companies to transform messages. It applies end-to-end encryption for information security and transmits data covertly by steganography technology for individual privacy. It also provides perfect forward security protection of users' historical messages. We design and implement MuChat, with this report on the security and performance of MuChat. The evaluation shows that MuChat has proved its high security in terms of running against active attacks, passive attacks, and traffic analysis methods in recent years. It supports all the functions of instant messages with low latency. For short words, it takes only 1–2 s while the base channels take about 0.5 s, and for 20-megabyte files messages with 35–45 s while about 30–40 s for base channels. In our view, this meets the instant communication needs of users. Besides, its architecture can fully support any other public communication channels which can be added by the user. [ABSTRACT FROM AUTHOR]

Polymer-based pure organic room-temperature phosphorescent materials have tremendous advantages in applications owing to their low cost, vast resources, and easy processability. However, designing polymer-based room-temperature phosphorescent materials with large Stokes shifts as key requirements in biocompatibility and environmental-friendly performance is still challenging. By generating charge transfer states as the gangplank from singlet excited states to triplet states in doped organic molecules, we find a host molecule (pyrrolidone) that affords charge transfer with doped guest molecules, and excellent polymer-based organic room-temperature phosphorescent materials can be easily fabricated when polymerizing the host molecule. By adding polyaromatic hydrocarbon molecules as electron-donor in polyvinylpyrrolidone, efficient intersystem crossing and tunable phosphorescent from green to near-infrared can be achieved, with maximum phosphorescence wavelength and lifetime up to 757 nm and 3850 ms, respectively. These doped polyvinylpyrrolidone materials have good photoactivation properties, recyclability, advanced data encryption, and anti-counterfeiting. This reported design strategy paves the way for the design of polyvinylpyrrolidone-based room-temperature phosphorescent materials. Room temperature phosphorescent polymers have potential in a range of applications, but achieving the desired properties can be challenging. Here, the authors report the development of such polymer materials by doping with polyaromatic hydrocarbons. [ABSTRACT FROM AUTHOR]

Cryptography secures data and serves to ensure the confidentiality of records. Playfair is a cryptographic symmetrical algorithm that encrypts statistics based on key costs. This secret is shared with an authorized person to retrieve data. In the conventional pattern, there is an area complexity and deficiency in letters, numbers, and special characters. This hassle has been overcome in previous studies by editing pattern dimensions. The fillers used throughout the enciphering were not eliminated during the retrieval process, which resulted in the indiscrimination of the retrieved statistics. The proposed method uses a separate quadrate pattern that strengthens the Playfair cipher and guarantees that the fillers are eliminated to ensure the authentic retrieval of records. The fillers indiscriminate and strengthen the set of rules in opposition to brute force and avalanche impact. The proposed algorithm was evaluated with a minimal change in the key, and was found to have an avalanche effect between 65% and 93.7%. The encrypted document is further encoded using the Lempel–Ziv–Markov chain algorithm (LZMA) to provide compressed second-level secured text with a compression ratio of 0.75 for a file size of 100 KB. The pattern was designed to subsidize the integrated characters found on the keyboard. [ABSTRACT FROM AUTHOR]

Nowadays, the graphical password has gained significant recognition and has become a subject of extensive investigation within the research community. The proliferation of Internet usage has resulted in individuals accessing various web applications from any location worldwide, utilizing personal computers, mobile phones, and other touch-enabled devices. However, individuals frequently employ passwords that are weak and commonly used due to their inability to recall complex passwords. This renders the systems susceptible to various forms of attacks. Hence, there is a requirement for an authentication scheme that possesses qualities such as resilience, ease of memorability, and security. Graphical passwords are significantly more effective than text-based passwords in terms of memorability. Nevertheless, numerous schemes are susceptible to various forms of attacks, such as shoulder surfing attacks, man-in-the-middle attacks, database attacks, random guess attacks, and so forth. Moreover, the compromise between security and usability concerns is evident in different graphical authentication schemes. Therefore, we present a novel graphical authentication scheme that ensures both security and usability. This scheme incorporates random graphical objects blended with a background image, resulting in the generation of a distinct graphical challenge. The objects that have been chosen must undergo verification through the utilization of an object detection algorithm known as YOLOv3. In order to strengthen the security of GPOD (Graphical password with object detection), user data is subjected to encryption and subsequently stored on the server, thereby mitigating the risk of potential database attacks. Additionally, the user data undergoes encryption prior to its transmission to the server in order to alleviate the risk of man-in-the-middle attacks. The proposed GPOD scheme is a straightforward, usable, resilient, shoulder-surf-resistant, and secure graphical authentication scheme. The scheme exhibits excellent performance, with an accuracy rate of up to 94.80% and a login time ranging from 9.61 to 14.56 seconds in two scenarios, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the quantum circuit implementations of the lightweight authenticated encryption algorithm ASCON by using the NOT gates, CNOT gates, Toffoli gates, measurements, and the dynamic quantum circuits. Firstly, the quantum circuit of addition of constants is realized by adding the NOT gates according to the position of 1 in round constants. Secondly, the quantum circuit of S-box of the permutation is synthesized according to the classical circuit diagram of S-box. Then the linear layer functions are expressed in matrix form, and their quantum circuits are synthesized according to Gaussian elimination. Finally, we synthesize the whole quantum circuits according to the general diagrams of the authenticated encryption algorithm ASCON. The correctness of the quantum circuits of the S-box and the linear layer was verified by the Aer simulator of the IBM Quantum platform. As far as we know, this is the first implementation of the quantum circuits for the Authenticated Encryption with Associated Data (AEAD) of ASCON in-place. The maximum quantum resources for the three ASCON authenticated encryption algorithms were estimated. The quantum circuit of ASCON-128 uses a total of 320 qubits, 30,639 NOT gates, 128,814 CNOT gates, 8064 Toffoli gates, 10,752 measurements, and 5376 dynamic quantum circuits. The quantum circuit of ASCON-128a uses a total of 320 qubits, 23,558 NOT gates, 98,144 CNOT gates, 6144 Toffoli gates, 8192 measurements, and 4096 dynamic quantum circuits. The quantum circuit of ASCON-80pq uses a total of 320 qubits, 30,736 NOT gates, 128,814 CNOT gates, 8064 Toffoli gates, 10,752 measurements, and 5376 dynamic quantum circuits. [ABSTRACT FROM AUTHOR]

Nowadays, users often opt to encrypt their sensitive data before outsourcing it to the cloud. While this encryption ensures data privacy, it compromises the search functionality. Public key encryption with keyword search (PEKS) has been posited as a potential solution to this challenge. However, many PEKS schemes are either inefficient or vulnerable to various types of attacks, such as inside keyword guessing attacks, outside keyword guessing attacks, etc. In response, we introduce a sustainable certificateless authenticated encryption system with a keyword search scheme. To the best of our knowledge, this proposed scheme is the first to consider multi-trapdoor indistinguishability within the certificateless primitive. Furthermore, our in-depth security analysis indicates that our scheme effectively protects against both online and offline keyword guessing attacksAdditionally, it ensures semantic security by offering both ciphertext indistinguishability and multi-trapdoor indistinguishability. Performance analysis results also suggest that our scheme is efficient and superior to existing alternatives. [ABSTRACT FROM AUTHOR]

In recent years, the popularity of blockchain application in healthcare sector has increased manifold due to features such as decentralized architecture, security, immutability and authentication. However, there are some challenges associated with blockchain-based healthcare frameworks. Firstly, blockchain provides data verifiability meaning that anything stored there is accessible to all nodes in the network for verification. However, it is unacceptable for sensitive medical data to be made accessible to everyone. Secondly, healthcare sectors are group-oriented and frequently involve open networks for communication which lead to data breaches and other security risks. Lastly, due to limited size of a block in blockchain, many hospitals prefer storing patient's data locally in a centralized server or opt for cloud storage which have their own threat vectors. In this work, we propose a three-phase unified approach to solve the above problems. Our framework utilizes proxy re-encryption and advanced cryptographic techniques to provide stringent access control. Next, a blockchain-based group encryption is proposed to ensure secure group communication. A group session key will be agreed upon by the authorized group members and used to protect sensitive patient information. Lastly, IPFS-based blockchain data storage is proposed to maintain data securely offsite. We provide a comparative security analysis of the proposed framework with other existing peer models and show that our framework offers several new security features. We also examine the security of the proposed framework using BAN logic. Furthermore, the proposed framework is simulated using the OMNET++ software, and its storage and computation costs are examined to establish its effectiveness. [ABSTRACT FROM AUTHOR]

With the number of attacks and cyberincidents affecting Internet of Things (IoT) devices on the rise, the need for carrying out forensic investigations to determine what has happened has grown at the same pace. However, due to the characteristics and requirements of this environment, the solutions used until now in the field are not suitable to be followed, as they are not able to guarantee the effective retrieval and study of the pieces of evidence. Under these circumstances, new ways of interacting with IoT units are needed, and the edge computing paradigm has emerged as an interesting asset to complement IoT networks. Attack, anomaly and intrusion detection, or data encryption are some of the fields in which this approach has been successfully applied. Following this concept, this article presents an IoT forensic methodology that integrates the edge computing technology in order to assist in the investigation process, trying to address some of the issues that are hindering the effectiveness of examinations in this scenario. In addition, this proposal is compared with the existing ones in the research community, and evaluated by testing it in two case studies representing real-life scenarios, ultimately demonstrating suitability to be used for IoT forensic examinations. [ABSTRACT FROM AUTHOR]

The sensing-as-a-service concept has been developed to address the challenges that future smart cities would have in managing an enormous number of sensors. Smart cities must find a solution to the enormous difficulty of effectively sharing user-confidential information without sacrificing quality, privacy, or interruption. The development of blockchain technology has the potential to overcome these problems, but the limitations of the current models include large storage, customizable blockchain, high communication overhead, and high processing costs. The proposed average hybrid leader optimization (AHLO)-PrivPresKey Gen model determined a blockchain-based secure information sharing scheme using a smart contract in a smart city. Blockchain-based smart contracts implement a highly complex midway illustration method to reduce the user's clarification burden. The proposed scheme makes utilize of different safety operators, like 3 Data Encryption Standard, segmenting interpolation, passwords, one time password token and etc. Furthermore, the data privacy phase is improved by utilizing various functionalities, such as dyadic product, Chebyshev polynomial, and keys, where the keys are optimally generated using AHLO. The designed AHLO is obtained by the consolidation of average and subtraction based optimizer and hybrid leader-based optimization. The designed model has delivered minimum memory of 30.5 MB, minimum computational time of 28.467 MB, maximum encryption quality of 0.808, maximum detection rate of 0.898, minimum responsiveness of 41.679 and minimum bandwidth of 95 that surpasses the conventional schemes. [ABSTRACT FROM AUTHOR]

In today's data-driven world, the exponential growth of digital information poses significant challenges in data management. In recent years, the adoption of cloud-based Electronic Health Records (EHR) sharing schemes has yielded numerous advantages like improved accessibility, availability, and enhanced interoperability. However, the centralized nature of cloud storage presents challenges in terms of information storage, privacy protection, and security. Despite several approaches that have been presented to ensure secure deduplication of similar EHRs, the validation of data integrity without a third-party auditor (TPA) remains a persistent task. Because involving a TPA raises concerns about the confidentiality and privacy of crucial healthcare information. To tackle this challenge, a novel cloud storage auditing technique is proposed that incorporates cross-patient block-level deduplication while upholding strong privacy protection, ensuring that EHR is not compromised. Here, we introduced blockchain technology to achieve integrity verification, thus eliminating the need for a TPA by providing a decentralized and transparent mechanism. Additionally, an index for all EHRs has been generated to facilitate block-level duplicate checks and employ a novel strategy to prevent adversaries from acquiring original information saved in the cloud storage. The security of the proposed approach is established against factorization attacks and decrypt exponent attacks. The performance evaluation demonstrates the superior efficiency of the proposed scheme in terms of file authenticator generation, challenge creation, and proof verification to other existing client-side deduplication approaches. [ABSTRACT FROM AUTHOR]

Cocrystal engineering is an efficient and simple strategy to construct functional materials, especially for the exploitation of novel and multifunctional materials. Herein, we report two kinds of nucleic-acid-base cocrystal systems that imitate the strong hydrogen bond interactions constructed in the form of complementary base pairing. The two cocrystals studied exhibit different colors of phosphorescence from their monomeric counterparts and show the feature of rare high-temperature phosphorescence. Mechanistic studies reveal that the strong hydrogen bond network stabilizes the triplet state and suppresses non-radiative transitions, resulting in phosphorescence even at 425 K. Moreover, the isolation effects of the hydrogen bond network regulate the interactions between the phosphor groups, realizing the manipulation from aggregation to single-molecule phosphorescence. Benefiting from the long-lived triplet state with a high quantum yield, the generation of reactive oxygen species by energy transfer is also available to utilize for some applications such as in photodynamic therapy and broad-spectrum microbicidal effects. In vitro experiments show that the cocrystals efficiently kill bacteria on a tooth surface and significantly help prevent dental caries. This work not only provides deep insight into the relationship of the structure-properties of cocrystal systems, but also facilitates the design of multifunctional cocrystal materials and enriches their potential applications. Cocrystal engineering is a promising strategy for constructing multifunctional materials. Here, the authors describe nucleic-acid-base cocrystal systems with different colors of phosphorescence from their monomeric counterparts and high-temperature phosphorescence with antimicrobial effects and data encryption applications. [ABSTRACT FROM AUTHOR]

Cryptography aims to design algorithms for the protection of important data transmitted over unsecured networks. These algorithms encrypt the data and render it incomprehensible even if it is captured by enemies. Today's data encryption standard is the AES algorithm. The most important and nonlinear unit in the AES algorithm is the s-box structure. The S-box provides the mixing process in the algorithm, that is, the confusion. An s-box structure with a high nonlinearity value greatly increases the security against various attacks. Therefore, s-box is of vital importance in an encryption algorithm. When we look at the literature, chaos-based s-box structures are frequently used. However, the nonlinearity value of s-boxes produced with chaos is low. In this article, a new, fast, and very simple method is proposed to increase the nonlinearity value of chaos-based s-box structures. In the proposed method, the nonlinearity value is calculated by changing the places of the elements respectively. When the nonlinearity value increases, the s-box is updated, and the next element is passed. In this way, many iterations are not looked at in vain. This saves a lot of time. As a result of the analysis, it has been proven that the s-box structure obtained by the proposed method is cryptographically strong and has a high nonlinearity value. It is thought that the proposed method will provide many advantages for future cryptography applications. [ABSTRACT FROM AUTHOR]

Quantum video encryption is an essential method for ensuring video content security. This paper proposes a quantum video encryption method based on bitplanes and improved Arnold scrambling. Firstly, we design a quantum video representation based on bitplanes (BRQV) and an improved Arnold scrambling to entangle video frames. Then, we use 2D quantum wavelet transforms and an improved logic mapping for frequency-domain encryption. The encryption security analysis reveals that the post-encryption SSIM value is close to 0.015, with an information entropy reaching 7.9541. The circuit complexity analysis shows that the proposed method can realize the video encryption using 16 n 3 + 272 n 2 - 88 n quantum basic gates with the circuit width 2 n + log 2 n + 5 . The best existing quantum video encryption method needs O (2 n n 3) quantum basic gates with the circuit width 3 n + 3 . Simulation results demonstrate that the proposed video encryption method exhibits low correlation coefficients and high efficiency. [ABSTRACT FROM AUTHOR]

Data privacy is becoming more important as the internet of things (IoT) rapidly expands. There are significant consequences for the broad adoption of the internet of things. Homomorphic encryption is one possible solution for protecting sensitive data in the IoT. However, there is a great deal of potential for efficiency gains. In order to get enough privacy and security, it is also crucial. Our homomorphic encryption technique for secure and private optical fibre communication is detailed in this publication. The data owner, fog server, and data consumers' privacy is the primary focus of the proposed architectural design. The performance of this research shows that our FCHE solution improves data security throughout the data storage operation and reduces encryption length. This research also aims to analyse the factors of optical fiber communication cost, storage cost, and computing cost in the context of FCHE. [ABSTRACT FROM AUTHOR]

The encryption of user data is crucial when employing electronic health record services to guarantee the security of the data stored on cloud servers. Attribute-based encryption (ABE) scheme is considered a powerful encryption technique that offers flexible and fine-grained access control capabilities. Further, the multi-user collaborative access ABE scheme additionally supports users to acquire access authorization through collaborative works. However, the existing multi-user collaborative access ABE schemes do not consider the different weights of collaboration users. Therefore, using these schemes for weighted multi-user collaborative access results in redundant attributes, which inevitably reduces the efficiency of the ABE scheme. This paper proposes a revocable and verifiable weighted attribute-based encryption with collaborative access scheme (RVWABE-CA), which can provide efficient weighted multi-user collaborative access, user revocation, and data integrity verification, as the fundamental cornerstone for establishing a robust framework to facilitate secure sharing of electronic health records in a public cloud environment. In detail, this scheme employs a novel weighted access tree to eliminate redundant attributes, utilizes encryption version information to control user revocation, and establishes Merkle Hash Tree for data integrity verification. We prove that our scheme is resistant against chosen plaintext attack. The experimental results demonstrate that our scheme has significant computational efficiency advantages compared to related works, without increasing storage or communication overhead. Therefore, the RVWABE-CA scheme can provide an efficient and flexible weighted collaborative access control and user revocation mechanism as well as data integrity verification for electronic health record systems. [ABSTRACT FROM AUTHOR]

The rapid development of network technology makes the implementation of ubiquitous tasks possible in which the Flying Ad-Hoc Network(FANET) plays an important role. Control stations distributed in different task areas can control UAVs in FANET to complete a complex task. However, the application of FANET brings about problems of identity security and data security. Attackers can impersonate legal entities to issue fake commands or modify task data, which will hinder the smooth execution of tasks. The existing centralized entity authorization method has the single point of failure problem, and the distributed method based on the blockchain does not consider the entity security and data security issues at the task level. Therefore, to protect the entity identity security and task data security of ubiquitous tasks in FANET, in this paper, we propose a blockchain-based ubiquitous entity authorization and management scheme (BUAMH), which uses smart contracts and homomorphic encryption technology to achieve entity registration, access control and dynamic task member management. Specifically, the blockchain is used to record and protect entity behavior information and the results of smart contracts. Five smart contracts are designed to realize the authorization and management of entity identity. Besides, Homomorphic encryption technology is used to manage UAVs while protecting entity privacy. Furthermore, a side-chain structure is constructed and an identity consensus mechanism(ICM) is proposed to manage multi tasks and speed up the consensus of miners, respectively. The experimental results show that the computational costs and communication costs of the proposed BUAMH are superior to the existing technology. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are revolutionizing lifestyles and social management. In IoT environments, there is a need to deploy a large number of sensing devices, which are typically resource-constrained, with limited computational power and communication resources. Due to its open nature, IoT applications confront potential security and privacy risks in exchange for convenience, with data privacy being a significant concern. Predicate encryption (PE) offers a promising approach to address this concern. However, most PE schemes are public-key cryptosystems, which are more expensive compared to symmetric cryptography. These costs are burdensome for resource-constrained devices, especially when dealing with massive amounts of data. A recent study by Viet et al. (ESORICS'2022) introduced a symmetric PE scheme. However, this scheme's representation of attributes and predicates is limited. To overcome this limitation, we propose a flexible symmetric PE scheme. In the proposed scheme, predicates and attributes are represented using vectors. Tokens are related to predicates, while ciphertexts are associated with attributes. The encrypted message can be decrypted when the values of the predicate vector and attribute vector are pairwise unequal. This scheme enables fine-grained access control over encrypted data, ensuring that users with any attribute value in the vector embedded in the ciphertext cannot decrypt it. The security analysis demonstrates that the proposed scheme effectively protects data privacy. Additionally, performance evaluations indicate that the scheme is efficient, providing a lightweight solution for data privacy in IoT environments. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is a rapidly growing technology that is deployed by various organizations due to the high level of scalability, elasticity, and virtualization it offers. However, security-related challenges arise due to insecure access control points. Security models in the cloud such as authentication, data recovery, confidentiality, accessibility, and data integrity. This includes the deployment model, barriers in cloud computing, cloud services, and security issues. Data security can be improved via the usage of encryption and decryption keys during transmission. However, the existing techniques often suffered from improved time consumption and complexity in handling more users. This paper mainly focuses on the data security issue with the outsourced sensitive data in the cloud server by designing a novel Modified Multi-scale Convolutional Neural Network-based Zebra Optimization (MMCNN-ZO) approach to improve the cloud data sharing system. The proposed MMCNN-ZO approach safeguards the crucial data effectively and outputs in the form of cipher text/image. Here, the weight functions of the MMCNN are tuned mechanically using the ZO algorithm for the enhanced encryption process. The decryption process is performed to obtain the data in user understandable format. These procedures make the proposed system effectively secure the data content by protecting against intrusions. The efficacy of the proposed MMCNN-ZO method is investigated using diverse evaluation indicators namely throughput, decryption, and encryption time. The proposed MMCNN-ZO algorithm achieved improvements in terms of throughput, encryption time, and decryption time of 7450 bits/sec, 4.82 ms, and 4.86 ms respectively compared to other existing methods of SVM, ANN, BPPSVC, and GBCRP. The experimental results inherit the better performances of the suggested MMCNN-ZO approach over other methods in terms of all metrics. [ABSTRACT FROM AUTHOR]

Data hiding along with the security plays a vital role in the wireless network. In recent years, object detection, Feature Extraction (FE) and correlation based technique are developed to predict the pixels that is suitable for embedding. However, traditional data hiding method still suffered in the selection of less distortion pixels. To overcome these shortcomings, this method proposes (i) Moving Object (MO) detection, (ii) FE, (iii) data hiding, and (iv) data encryption. Initially, the MO in the video are detected using optical flow and tracking the weight of MO. Next, data hiding features are extracted using Optimization Fruit fly Algorithm (OFA) with Differential Evolution (DE) and Opposition Based Learning (OBL) algorithm. Then, a modified data hiding techniques based on correlation, regression and pixel segmentation with Prediction Error (PE) techniques are used for improving the performance. Finally, the hidden data is encrypted using Binary Tree Structure (BTS) to provide additional layer of security. Experimental result shows the proposed method is superior to other state-of-the-art methods in terms of Peak-Signal-to-Noise Ratio (PSNR), embedding capacity and security analysis. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) refers to a system of interconnected equipment, sensors, and devices deployed within industrial environments whose primary function is to collect and facilitate the exchange of data. IIoT has gained significant traction in several domains, encompassing remote monitoring, equipment management, and condition tracking. The utilization of IIoT in different environments gives rise to apprehensions regarding the security of data. As a result of this, it is imperative to ensure that rigorous safety measures are in place to safeguard the confidentiality of the sensitive information being transmitted. The utilization of elliptic curve cryptography (ECC) and quantum key exchange (QKE) in IIoT networks holds promise for achieving robust and predictable security measures. ECC is a widely adopted type of public-key cryptography due to its efficiency and security. It has gained significant popularity in contemporary systems. QKE is an innovative methodology that originates from the fundamental principles of quantum physics. The system has the ability to generate a secret key that is impervious to hacking attempts, as it leverages natural principles instead of conventional mathematical procedures. Due to this characteristic, quantum key distribution (QKD) is widely recognized as one of the most reliable methods for key exchange. The combination has led to the creation of a novel cryptographic model that integrates elliptic curve cryptography (ECC) and quantum key exchange (QKE) techniques for the generation and distribution of cryptographic keys. The model leads to an increase in the base probability of the Bennett–Brassard protocol (BB84) from 0.5 to 1. Subsequently, the keys that are produced are employed for the encryption of data through a streamlined and adapted variant of the Advanced Encryption Standard (AES), thereby expediting the secure exchange of data while upholding its level of safeguarding. [ABSTRACT FROM AUTHOR]

This paper presents an image encryption scheme with data and appearance security, by adopting inter-intra-block scrambling and weighted diffusion. The 2D robust hyper-chaotic map with flexible geometric distribution and rich hyper-chaotic parameter space is employed to generate the key stream for encryption, by considering the characteristics of plaintext. The plain image is first preprocessed by Huffman coding for getting compressed image. Then, the compressed image is divided into four sub-blocks and is further permuted by the designed inter-intra-block scrambling scheme, which can improve the scrambling effect by making the pixel far away from the original adjacent pixels. After that, a weighted diffusion method strongly related to plaintext and key stream is introduced to diffuse the shuffled image to obtain the noise-like cipher image. And in pursuit of higher security, the meaningless noise-like image is embedded into host image to create the visually meaningful cipher image. A series of experiment tests and analyses are carried out to further demonstrate the excellent performances of the encryption scheme. [ABSTRACT FROM AUTHOR]

With the advancement of content-based medical image retrieval (CBMIR) technology which is used as a convenient assistant for medical diagnosis. However, the potential risk of privacy disclosure in CBMIR remains a concern due to the involvement of patients' sensitive information in medical images. To address this issue, we have proposed an efficient scheme to achieve privacy-preserving medical image retrieval, named PMIR. The primary objective of PMIR is to enhance the accuracy of medical image retrieval while ensuring privacy protection. In PMIR, medical institutions with large repositories of medical images can securely upload to the cloud server with their image data and encryption indexes. Subsequently, users who successfully registered from medical institutions are able to enjoy convenient image retrieval services without revealing their sensitive information and query attributes to the cloud server. The proposed approach emphasizes a privacy-preserving policy mechanism, which empowers users with the right to choose rather than relying solely on service providers. Through rigorous security analysis, it is demonstrated that PMIR can effectively withstand various known security threats. Additionally, experimental results highlight the substantial reduction in communication overhead achieved by PMIR, ultimately providing a seamless and secure search experience for users. [ABSTRACT FROM AUTHOR]

Supply chain management has become increasingly complex, leading to information loss and delayed transmission, exacerbating the bullwhip effect. Furthermore, the need for more reliable information storage, traceability, and accountability has slowed supply chain management. In this context, blockchain technology (BCT) solves the bullwhip effect problem, as it supports distributed networking, information synchronization among nodes, digital encryption, traceable information, and unchangeable block content. This paper proposes using public blockchain to solve the bullwhip effect problem in the supply chain (SC), which has yet to be discussed in published academic research papers. To this end, a framework has been developed to determine the readiness of a supply chain for blockchain adoption. This framework includes a checklist to evaluate the motivation, timing, and appropriate platform for adopting blockchain. The framework has been validated using a case study of personal computer devices, which were affected by the Covid-19 pandemic and resulted in semiconductors supply chain disruption. The study involved polling four executives The study involved polling four executives (of large organizations in Egypt and some Middle Eastern countries) representing different stages in the supply chain, they are retail, distribution, manufacturing, and supply. The results showed that while the executives recognized the benefits of adopting blockchain, they were still determining if it was the right time for adoption and did not see the technology ready for their supply chains. Finally, some public blockchain platforms were introduced to help businesses choose the most suitable platform for adoption. [ABSTRACT FROM AUTHOR]

Encryption chips are specialized integrated circuits that incorporate encryption algorithms for data encryption and decryption, ensuring data confidentiality and security. In China, the domestic SM4 algorithm is commonly utilized, as opposed to the international AES encryption algorithm. These widely implemented encryption standards have been proven to be difficult to crack through crypt analysis methods Currently, power consumption side-channel attacks are the most prevalent method. They involve capturing power consumption data during the encryption process and subsequently recovering the encryption key from this data. The two leading methods are Differential Power Analysis (DPA) and machine learning techniques. DPA does not necessitate prior knowledge but relies heavily on the number of power consumption curves. With only 50 power consumption data points, the accuracy is a mere 80%. Machine learning methods require prior knowledge, achieving an accuracy rate above 95% with only 30 power traces, albeit with training times typically exceeding 15 min. In this paper, a distributed energy analysis attack approach was presented based on Correlation Power Analysis (CPA). The power consumption data was divided into 16 subsets, with each subset corresponding to 8 bytes of the key. By training each subset separately, the 8-byte key's corresponding power consumption data is reduced to only 100 dimensions, resulting in a 76% decrease in cracking time and a 3% improvement in cracking accuracy rate.This article also trains a more complex 256 classification model to directly crack the final key, achieving a success rate of 28% in cracking 128-bit passwords with only 1 power trace [ABSTRACT FROM AUTHOR]

The principal constituent of a block cipher in symmetric-key cryptography is the Boolean function, determining the substitution box (S-box). Block ciphers rely totally on S-boxes with excellent nonlinearity and upright cryptographic structures. In AES, an 8 × 8 S-box is a 16 × 16 lookup table over the Galois field GF 2 8 , that occupies 8 × 28 bytes storage of computer memory. By using traditional method to construct a 24 × 24 S-box over Galois field GF 2 24 , which lodges a storage memory of 24 × 224 bytes in traditional sense. Thus, the memory storage does not support a 24 × 24 S-box over a very larger order Galois field like GF 2 24 . A resolute of this difficulty is possibly coming out from the algebraic structure of the commutative finite chain ring F q [ x ] < x k > = ∑ i = 0 k - 1 x i F q. In this study, a subgroup of the multiplicative group of units of the chain ring F 2 [ x ] < x 24 > = ∑ i = 0 23 x i F 2 is considered to construct a 24 × 24 S-box that occupy just 24 × 28 bits storage memory of computer. The proposed S-box has a substantial potential to create confusion during substitution phase of the color image enciphering algorithm. While for the permutation component of the algorithm, DNA transform is applied for creating diffusion in the pixels of the color image. The proposed RGB image encryption attains the standard optimum level when compared it to the DNA and chaos-based image encryption techniques. [ABSTRACT FROM AUTHOR]

After the tremendous development and fast growth in communication systems and electronic devices, information security and preservation have become one of the most important procedures to be considered when transferring data between two parties. Encryption and data hiding are two of the most important techniques to protect the information sent over networks and the Internet. In this paper, a new technique of encryption and concealment is used through the following steps: First, the secret information is encrypted by using developed DNA to get (S1), second, by scrambling the encrypted data, the (S1) is diffused by using a zigzag method to get (S2), third, re-obfuscation the (S2) by using developed zigzag method through using Tinkerbell technique to get (S3), fourth, perform XoR operation between the (S3) and the Tinkerbell technique to get (S4), and fifth, the (S4) is hidden in the random locations of the cover image based on the LSB technique and the Tinkerbell technology, where the Tinkerbell has incredibly excellent characteristics and used to generate random sequence numbers twice with different initial and parameters value, once for the encryption process for scrambling secret information and once for the hiding process for determining hidden locations. Several metrics were used to evaluate the system's efficiency: histogram, PSNR, MSE, mean absolute error, bit error rate, encryption quality, Unified Average Changing Intensity, hamming distance, entropy, and correlation coefficient. The experimental results display that the suggested method has a good performance and reliability, high security, and greater embedding capacity and ensures higher image quality; also, the proposed method presented preferable outcomes when compared to other approaches. [ABSTRACT FROM AUTHOR]

Cloud storage is the ideal solution for outsourcing big data since the cloud can store a large amount of data. Cloud storage, on the other hand, raises additional problems about data duplication, fine-grained access control, and privacy, all of these factors are crucial for cloud large data storage. Data duplication approaches based on encrypted data schemes now available do not provide for fine-grained access control. This paper proposes a secure framework for managing data using rapid asymmetric maximum based dynamic size chunking and fuzzy logic for deduplication. Chunking, fingerprinting, hashing, and writing are the four main process of the proposed method. Initially, chunking is done to split the files into chunks. Rapid Asymmetric Maximum (RAM) based Dynamic Size Chunking (DSC) is used in the proposed method. These chunked files are then fingerprinted using hashing process for ensuring data authentication. Then B-tree indexing approach is used in the proposed method in order to keep the fingerprinted in an organized state. General Type2-Fuzzy logic is using Ant Lion Optimization (ALO) is used for detecting duplicate files in the documents. In the cloud storage platform, only non-duplicate documents are safely kept. The Triple Data Encryption Standard is used to do a security study before outsourcing non-duplicate data to a third-party cloud server. The total computation time of the proposed technique is 0.4 s in the inline phase and 0.04 s in the offline phase, and the deduplication ratio is 95% in the inline phase and 90% in the offline phase. This proposed deduplication approach requires less storage, which reduces memory use and processing time. [ABSTRACT FROM AUTHOR]

Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection. [ABSTRACT FROM AUTHOR]

Electronic payment (E-payment) systems rely heavily on secure communication, typically achieved through encryption processes in cryptography. Traditional cryptography often relies on complex mathematical problems for encryption, such as difficult decompositions or extracting discrete logarithms. With the development of quantum computing, the security of these classical encryption methods is under threat. To address this challenge, quantum E-payment protocols have emerged as a potential solution. However, the practical quantum E-payment protocols are limited due to the channel transmission efficiency decreasing exponentially with distance. This is further constrained by the Pirandola–Laurenza–Ottaviani–Banchi (PLOB) bound. To address this challenge, we propose a twin-field quantum encryption protocol for E-payment based on blockchain. In our protocol, we propose a twin-field quantum encryption method to authenticate identity and detect eavesdropping simultaneously. It not only eliminates side-channel attacks on the detectors but also has the potential to break the PLOB bound. Our security analysis shows that our E-payment protocol exhibits security properties such as blindness, unforgeability, and undeniability. Furthermore, our protocol demonstrates withstand against common inside and outside attacks. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is an emerging field in the IT sector due to its prospective, progress and incredible impact on the development of that sector. CC is a centralized and distributed network comprised of interrelated and interconnected systems, and the whole universe uses the cloud for the ultimate portion of data transmission. However, the cloud cannot provide security assurance, which is one of the main drawbacks of the cloud. Blockchain is the fascinating massive attention in the cloud field because of its security features. It consists of a chain of several blocks composed of multiple transactions. These data available in blocks are encrypted and cannot be altered by any in the world. Several emerging applications are affected by insurance companies, anomalies, thieves, and intruders fetching individual data and disturbing the user's routine. This paper develops a blockchain-based modified infinite chaotic elliptic cryptography (MICEC) to improve and tighten the security in the cloud. It contains three stages: protection of authentication, ownership protection, and identity mapping validation. At first, MICEC is employed for an authentication process that combines the infinite elliptic curve cryptography and modified chaotic neural network for pair of key generation and data encryption. In addition, the hash function is generated with the aid of the improved message digest 5 algorithm. Second, the improved Message Content Recommendation Algorithm with Latent Dirichlet Allocation is used to compute the score values. At last, cosine similarity matching criteria is computed in the validation based on the digests. The proposed work is simulated using the JAVA programming language with NetBeans IDE. The performance is evaluated and compared to other methods. [ABSTRACT FROM AUTHOR]

Optical image/data encryption techniques are mostly based on the manipulation of spatial distributions of light's amplitude, phase, and polarization. Information encoding with phase involves complex interferometric set-up and polarization encoding requires Stoke's parameter measurement. Hence, they create difficulties in optical implementation. Considering the practical limitations, in this study, we demonstrate a method of single-shot intensity recording-based color image encryption by encoding the information in binary polarization states. The proposed method does not require Stoke parameter calculation. As a proof-of-concept, we demonstrated the technique with coherent and partially coherent light sources. Use of partially coherent light overcomes the speckle problem and makes the system cost-effective, useful for practical applications. [ABSTRACT FROM AUTHOR]