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With the massive deployment of the Internet of things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing federated learning schemes either take expensive computation costs (e.g., public key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving federated learning scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

The use of the internet in this modern era is increased many fold. The communications between different peers take place in digital form. While sharing the messages between different recipients, the confidentiality of the messages is very important. For creating the high level of security while sharing the secret messages, the cryptographic algorithms are introduced along with steganography. Image Steganography is a methodology used to hide the messages inside of the cover image. Initially, the secret information is encrypted by using the RSA Algorithm. Then the encrypted secret information is hidden in the Least Significant Bit (LSB) of the different components of the color image in such a way that the original quality of the image to be minimized. The recipient of the message is able to retrieve the encrypted secret message from the LSB bit of stego_image and then the cipher text is converted into original plain text by using the RSA algorithm. The proposed algorithm verified and analysed its performance against the different combinations of key pairs.

In quantum cryptography, quantum key distribution protocols (QKDPs) make use of quantum mechanisms to circulate treatment secrets as well as public conversations to look for viewers and confirm the accuracy of a session key. Having said that, social conversations call for additional communication rounds in between a sender as well as the receiver and expense precious qubits. By comparison, timeless cryptography supplies handy techniques that make it possible for efficient key confirmation and user verification.

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. The framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on SPM, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experiments indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

We study the problem of multi-user dynamic searchable symmetric encryption (DMUSSE) where a data owner stores its encrypted documents on an untrusted remote server and wishes to selectively allow multiple users to access them by issuing keyword search queries. Specifically, we consider the case where some of the users may be corrupted and colluding with the server to extract additional information about the dataset (beyond what they have access to). We provide the first formal security definition for the dynamic setting as well as forward and backward privacy definitions. We then propose SE, the first provably secure DMUSSE scheme and instantiate it in two versions, one based on oblivious data structures and one based on update queues, with different performance trade-offs. Furthermore, we extend SE to support verifiability of results. To achieve this, users need a secure digest initially computed by the data owner and changed after every update. We efficiently accommodate this, without relying on a trusted third party, by adopting a blockchain-based approach for the digests dissemination and deploy our schemes over the permissioned Hyperledger Fabric blockchain. We prototype both versions and experimentally evaluate their practical performance, both as stand-alone systems and running on top of Hyperledger Fabric.

A blockchain is a revolutionary computational data structure that enables an open, public, distributed ledger with a wide range of uses. Any new cryptographic application, on the other hand, should take into consideration expected technical improvement during the lifetime of any possibly deployed systems, many of which will be in use for decades. This document addresses the weaknesses of blockchain technology as a result of the development of quantum computers, as well as general recommendations for making blockchain more resistant to such technological breakthroughs. As a result, utilizing a special approach, this work shows many steps of the suggested electronic voting system. The method contains the idea of electronic voting using Quantum key distribution and blockchain security. Explicitly an extended version of the blockchain-based e-voting system to several networks applications. This work also describes the architecture, design, and design constraints of such a voting system in our society with implementation implications.

We propose a decentralized e-voting protocol that is coercion-resistant and vote-selling resistant, while being also completely transparent and not receipt-free. We achieve decentralization using blockchain technology. Because of the properties such as transparency, decentralization, and non-repudiation, blockchain is a fundamental technology of great interest in its own right, and it also has large potential when integrated into many other areas. We prove the security of the protocol under the standard DDH assumption on the underlying prime-order cyclic group (e.g. the group of points of an elliptic curve), as well as under standard assumptions on blockchain robustness.

Key assignment and key maintenance in encrypted networks of resource-limited devices may be a challenging task, due to the permanent need of replacing out-of-service devices with new ones and to the consequent need of updating the key information. Recently, Aragona et al. proposed a new cryptographic scheme, ECTAKS, which provides a solution to this design problem by means of a Diffie-Hellman-like key establishment protocol based on elliptic curves and on a prime field. Even if the authors proved some results related to the security of the scheme, the latter still lacks a formal security analysis. In this paper, we address this issue by providing a security proof for ECTAKS in the setting of computational security, assuming that no adversary can solve the underlying discrete logarithm problems with non-negligible success probability.

Since the nineties, the Man-in-The-Middle (MITM) attack has been one of the most effective strategies adopted for compromising information security in network environments. In this paper, we focus our attention on ARP cache poisoning, which is one of the most well-known and more adopted techniques for performing MITM attacks in Ethernet local area networks. More precisely, we will prove that, in network environments with at least one malicious host in the absence of cryptography, an ARP cache poisoning attack cannot be avoided. Subsequently, we advance ArpON, an efficient and effective solution to counteract ARP cache poisoning, and we use a model-checker for verifying its safety property. Our main finding, in accordance with the above impossibility result, is that the only event that compromises the safety of ArpON is a cache poisoning that nevertheless is removed by ArpON itself after a very short period, thus making it practically infeasible to perpetrate an ARP cache poisoning attack on network hosts where ArpON is installed.

The public cloud data integrity auditing technique is used to check the integrity of cloud data through the Third Party Auditor. In order to make it more practical, we propose a new paradigm called integrity auditing based on the keyword with sensitive information privacy for encrypted cloud data. This paradigm is designed for one of the most common scenario, that is, the user concerns the integrity of a portion of encrypted cloud files that contain his/her interested keywords. In our proposed scheme, the TPA who is only provided with the encrypted keyword, can audit the integrity of all encrypted cloud files that contain the user's interested keyword. Meanwhile, the TPA cannot deduce the sensitive information about which files contain the keyword and how many files contain this keyword. These salient features are realized by leveraging a newly proposed Relation Authentication Label (RAL). The RAL can not only authenticate the relation that files contain the queried keyword, but also be used to generate the auditing proof without sensitive information exposure. We give concrete security analysis showing that the proposed scheme satisfies correctness, auditing soundness and sensitive information privacy. We also conduct the detailed experiments to show the efficiency of our scheme.

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this paper, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of our system components and the integrated system.

The privacy and confidentiality of our data are more fundamental to our existence than ever, especially with our growing dependence on online services and the transfer of information, be it for convenience or the unfortunate COVID 19 pandemic. Traditional security measures such as cryptography and steganography are essential. However, they are not sufficient anymore as when one party maintains complete control over the data, they may intentionally, or not, lose or reveal it. Thus, secret sharing schemes were introduced to address applications that require collective agreement by authorized personnel to access or modify, such as missile launching, sophisticated medical agreements, or high-level bank transfers. In standard secret sharing, the target key is distributed among several authorized participants in a way that only the intended group of them are needed to reconstruct the original target key. These schemes became popular as they fortified the security of both cryptography and steganography and accomplished extraordinary results combined with each. This work focuses on two particular secret sharing techniques known as counting-based secret sharing and matrix-based secret sharing, which is based on the former. These methods are simple and intuitive. Consequently, they are prone to attacks that may result in the successful guessing of the key. In this work, not only are the shares hidden but they are also encrypted beforehand so that should they be intercepted, the adversary cannot decipher them. In other words, two layers of security are added to the secret-sharing method: steganography and cryptography. We studied two image steganography methods: least significant bit (LSB) and discrete wavelet transform (DWT), each combined with XOR encryption for security and robustness acceptability verification. The research results demonstrated that the use of steganography and encryption along with the matrix-based secret sharing did not affect the quality of operation nor compromised the security of information presenting attractive remarks. © 2021 The Authors

Blockchain is a chain of blocks where each block contains a set of transactions that are digitally signed by its verifier and stored across the distributed network so that all the legitimate stakeholders can access/verify them. Due to the attributes of Blockchain such as decentralization, immutability, auditability, transparency, and cryptographic security, it offers various benefits to different domains such as cryptocurrency, financial sectors, private/public segments, insurance, healthcare, supply chain management, Internet of Things, etc. However, the technology is in its early stage and still, there is a range of concerns that are yet to be addressed before its wide adoption. Through this paper, we intend to cover extensive study on the Blockchain that includes taxonomy, application/use-cases, consensus mechanisms, prospective research, future directions, and related technologies. This paper also aims to discuss the opportunities, benefits, and challenges of Blockchain technology and to assist the research community in understanding the same.

Due to the big data blowout from the Internet of Things (IoT) and the rapid development of cloud computing, outsourcing computation has received considerable attention recently. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis. However, none of them are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, we propose a new outsourcing polynomial computation scheme by employing a variant of Horners method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and worker if the computing result can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we implement a prototype of our proposal and run it on an Ethereum test net. The experimental results demonstrate that our proposal is efficient in terms of computational cost.

Web3.0, often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate Web3.0 from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for Web3.0. Our definition is not the only way of understanding Web3.0, yet, it captures the fundamental and defining trait of Web3.0, and meanwhile it is has two desirable properties. Under this definition, we articulate three key infrastructural enablers for Web3.0: individual blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers. While innovations in all categories are necessary to fully enable Web3.0, in this paper, we present a design for the third enabler, namely HyperService, that delivers interoperability and programmability across heterogeneous blockchains and state publishers. HyperService is powered by two innovative designs: a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in 62,000 lines of code to demonstrate its practicality, usability, and scalability.

With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability - The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments - The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.

Secure and efficient analytic over sensitive data becomes nowadays a big challenge for some enterprises (medical health care, insurance, etc). While these companies are collecting a large amount of sensitive data, cloud system is offering important opportunities for outsourcing of storage and computation. Thus, preserving the data privacy while maintaining the efficiency in computation at the cloud is considered an attractive research topic for computer scientists in academia as well as in industry. Homomorphic Encryption (HE) came as a new cryptographic topic that allows computation over encrypted data at the cloud side. Paillier is a famous HE scheme, that allows secure addition and average calculation over encrypted data. It presents the efficiency in implementation while preserving the security. In this paper, a new HE scheme competent for Paillier crypto-system is proposed. The presented scheme is called Secure Analytic using Vector based Homomorphic Operation (SAVHO). A deep security analysis has shown that the new scheme is resistant against several types of attacks. In addition, performance analysis has shown that SAVHO is a practical scheme by comparison with Pailler crypto-system. Different implementations are achieved under Python using SageMath Library.

To manage outsourced encrypted data sharing in clouds, attribute-based proxy re-encryption (ABPRE) has become an elegant primitive. In ABPRE, a cloud server can transform an original recipient's ciphertext to a new one of a shared user's. As the transformation is computation consuming, a malicious cloud server may return an incorrect re-encrypted ciphertext to save its computation resources. Moreover, a shared user may accuse the cloud server of returning an incorrect re-encrypted ciphertext to refuse to pay the cost of using the cloud service. However, existing ABPRE schemes do not support a mechanism to achieve verifiability and fairness. In this paper, a novel verifiable and fair attribute-based proxy re-encryption (VF-ABPRE) scheme is introduced to support verifiability and fairness. The verifiability enables a shared user to verify whether the re-encrypted ciphertext returned by the server is correct and the fairness ensures a cloud server escape from malicious accusation if it has indeed conducted the re-encryption operation honestly. Additionally, we conduct a performance experiment to show the efficiency and practicality of the new VF-ABPRE scheme.

Traditional cryptographic approaches may not always be suitable for cyber-physical systems (CPSs). In this correspondence, we present a control-theoretic approach allowing a networked controller and a smart actuator of a stochastic CPS to agree on a common secret key without resorting to classical cryptographic approaches. More precisely, by utilizing the asymmetry in the system model knowledge available to the control system defender and to the eavesdropper, we propose a key-agreement scheme utilizing a simultaneous input and state estimation algorithm. The validity of the proposed solution is shown through a numerical example.

The rapid growth of the globalized integrated circuit (IC) supply chain has drawn the attention of numerous malicious actors that try to exploit it for profit. One of the most prominent targets of such parties is the third-party intellectual property (3PIP) vendors and their circuit designs. With the increasing number of transactions between vendors and system integrators, the threat of IP reuse and piracy has become a significant consideration for the IC industry. What is more, the correctness of 3PIP designs should be verified before integration, imposing another challenge for 3PIP vendors since they have to prove the functionality of their designs to system integrators while protecting the privacy of the circuit implementations. To eliminate this deadlock, we utilize the cryptographic technique of “zero-knowledge proofs” to enable 3PIP vendors to convince system integrators about various functional properties of a circuit (e.g., area, power, frequency) without disclosing its netlist (i.e., in zero-knowledge). Our approach comprises a circuit compiler that transforms arbitrary netlists into a zero knowledge-friendly format and a library of modules that provide cryptographic guarantees for various properties of the netlist while hiding the actual gates. We evaluate our method using combinational and sequential circuits from the ISCAS and ITC benchmark suites.

In cloud computing, resources are usually in cloud service provider's network and typically accessed remotely by the cloud users via public channels. Key agreement enables secure channel establishment over a public channel for the secure communications between a cloud user and a cloud service provider. Existing key agreement protocols for cloud computing suffer from some challenges, e.g., realizing low connection delay, eliminating certificate management problem, enhancing user privacy and avoiding bad randomness. To tackle these challenges, we propose a certificateless 0-RTT anonymous AKA protocol against bad randomness for secure channel establishment in cloud computing. As a 0-RTT protocol, it significantly speeds up the efficiency of the secure channel establishment process. Further, our protocol does not need for the certificates to bind a public key with an entity's identity and hence solves the certificate management problem. Finally, concrete security analysis of the protocol is also proposed. The protocol not only satisfies the traditional security attributes (e.g., known-key security, unknown key-share), but also strong security guarantees, i.e., user privacy and bad randomness resistance.

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey study, we first examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

Heterogeneous SoCs integrate FPGAs and microprocessor cores on the same fabric to accelerate applications such as cryptography and deep learning. Since FPGAs share resources with the microprocessor cores, they can launch non-cacheable SDRAM transactions through direct FPGA-to-microprocessor SDRAM interface. Therefore, if the FPGA 3rd party IPs (3PIPs) are malicious, they can launch rowhammer attacks on the SDRAM. Today’s countermeasures based on performance counters cannot detect these attacks because memory transactions from FPGAs do not pass through the cache. In addition, today’s countermeasures that count the frequency of activation of memory rows cannot identify the IP that launches the attack from the FPGA. We present a security solution that monitors the SDRAM transactions from IPs on the FPGA to each bank of the microprocessor SDRAM through the FPGA-to-microprocessor SDRAM interface. The proposed monitor is implemented on the FPGA fabric. It can detect attempts to launch a rowhammer attack before it causes bit flips in the SDRAM. It utilizes 6.3% of the adaptive logic modules (ALMs) available in an Intel Cyclone V FPGA, when multiple IPs are monitored.

Digital technologies had an effect on people's lives. The majority of these digital devices rely on cloud storage to meet their memory needs. Hundreds of thousands of images, videos, and audio files are being transferred to cloud storage. Thousands of people around the world access these media every second. Unauthorized access to these media must be avoided. One of the weak points for data breaches is the user-end encryption. This paper suggests a strategy for improving cloud data protection by combining the AES and blowfish encryption and decryption algorithms. AES-256 is used as the first layer, followed by blowfish as the second layer, in the hybrid solution. The output of the first layer is input to the second layer and the final result is analyzed. The proposed method also discusses other combined approaches such as AES with other traditional algorithms but the proposed method gives significant results compared to other approaches.

Despite increasingly emerging applications, a primary concern for blockchain to be fully practical is the inefficiency of data query. Direct queries on the blockchain take much time by searching every block, while indirect queries on a blockchain database greatly degrade the authenticity of query results. To conquer the authenticity problem, we propose a Verifiable Query Layer (VQL) that can be deployed in the cloud to provide both efficient and verifiable data query services for blockchain systems. The middleware layer extracts data from the underlying blockchain system and efficiently reorganizes them in databases. To prevent falsified data from being stored in the middleware, a cryptographic fingerprint is calculated based on each constructed database. The database fingerprint will be first verified by miners and then written into the blockchain. Moreover, public users can verify the entire databases or several databases that interest them in the middleware layer. We implement VQL together with the verification schemes and conduct extensive experiments based on a practical blockchain system. The evaluation results demonstrate that VQL can efficiently support various data query services and guarantee the authenticity of query results for blockchain systems.

With the rapid growth of data sources, Big data security in Cloud is a big challenge. Different issues have ascended in the area of Big data security such as infrastructure security, data privacy, data management and data integrity. Currently, Big data processing, analytics and storage is secured using cryptography algorithms, which are not appropriate for Big data protection over Cloud. In this paper, we present a solution for addressing the main issues in Big data security over Cloud. We propose a novel system architecture called the Secure Authentication and Data Sharing in Cloud (SADS-Cloud). There are three processes involved in this paper including (i). Big Data Outsourcing, (ii). Big Data Sharing and (iii). Big Data Management. In Big data outsourcing, the data owners are registered to a Trust Center using SHA-3 hashing algorithm. The MapReduce model is used to split the input file into fixed-size of blocks of data and SALSA20 encryption algorithm is applied over each block. In Big data sharing, data users participate in a secure file retrieval. For this purpose, user's credentials (ID, password, secure ID, and current timestamp, email id) are hashed and compared with that stored in a database. In Big data management, there are three important processes implemented to organize data. They are as follows: Compression using Lemperl Ziv Markow Algorithm (LZMA), Clustering using Density-based Clustering of Applications with Noise (DBSCAN), and Indexing using Fractal Index Tree. The proposed scheme for these processes are implemented using Java Programming and performance tested for the following metrics: Information Loss, Compression Ratio, Throughput, Encryption Time and Decryption Time.

Forward privacy in existing searchable symmetric encryption approaches generally supports only keyword searches, rather than range searches. In addition, existing approaches that support range search over encrypted datasets have limitations associated with efficiency, accuracy, and security. Seeking to address these limitations, we formally propose the definition of the forward privacy for secure range searches. Also, we demonstrate how three widely-used cryptographic tools— order-preserving encryption (OPE), pseudorandom function, and one-time pad–can be leveraged to design an efficient, accurate, and forward secure (EAFS) searchable encryption scheme supporting range search in encrypted numerical databases. In the proposed EAFS scheme, a trapdoor only matches the last data record that satisfies the search range, and the other results are found iteratively using the previous result. The chain-like search and the embedded ciphertexts of OPE simultaneously guarantee its efficiency, forward privacy, and accuracy. We also use the simulation-based method to demonstrate that our scheme is secure. Then, we implement EAFS in Microsoft Azure and evaluate its performance. The evaluation findings demonstrate that our proposed scheme supports secure range search practically and efficiently.

The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.

Radio Frequency Identiocation (RFID) is a key emerging technology to improve data sharing in item distribution systems. By attaching RFID tags to items, item related data can be bound to items and participants involved in an item distribution system can directly store, access and update the data by interrogating the tags. Such a iexible data access manner of RFID technology, however, raises privacy and security concerns. In this paper, we focus on a special item distribution system named RFID-enabled Third-party Distribution (RTD) system and identify two inherent security and privacy requirements. We further design a Secure RTD system called Ants, which uses cryptography to protect item messages carried by tags to satisfy both of the requirements while preserving the iexible data access manner of RFID technology. Ants introduces two new techniques namedcommitmentaccumulationandselectivemessageprooffor memory-constrained tags to carry long crypto-item messages. We conduct theoretical analysis and experiments to demonstrate the security and efficiency of Ants.

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this paper proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy-preservation while providing secure data access control in VDF is a non-trivial challenge. To mitigate this issue, in this paper, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper-resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.

The recent appearance of Internet of Things (IoT) technologies applied in the maritime industry has introduced the Internet of Ships (IoS) paradigm. By leveraging IoS and deep learning (DL), various DL-based fault diagnosis methods have been proposed to improve shipping companies’ maintenance performance and reduce operational costs. However, the traditional centralized learning approach (CL), which centralizes the data resources of different shipping companies to a cloud server for model training, is restricted in real industrial scenarios due to privacy concerns and business competitions. In this paper, we propose a novel adaptive privacy-preserving federated learning approach, named AdaPFL, for fault diagnosis in IoS, which can organize different shipping agents to collaboratively develop a model by sharing model parameters with no risk of data leakage. First, we use two common tasks as examples to demonstrate that a small part of the model parameters might reveal the shipping agents’ raw information. Based on this, the Paillier-based communication scheme is designed to preserve the raw information of the shipping agents. Further, to deal with the harsh marine environment, a control algorithm is proposed to adaptively change the model aggregation interval during the training process for reducing cryptography computation and communication costs. Theoretical analysis and experiments prove the high effectiveness of the AdaPFL on a real non-independent and identically distributed (Non-IID) fault dataset.

The field of cryptography has endeavored to solve numerous security problems. However, a common premise of many of those problems is that the encryptor always generates the ciphertext correctly. Around 10 years ago, this premise was not a problem. However, due to the rapid development and the use of the cloud, which has introduced various access policies and functionalities to provide higher security, it is not correct to assume that this premise is always applied. A "Fake Policy Attack", which we introduce in this paper, is an attack that incorrectly sets the access policy of the ciphertext against the system rules so that users who do not meet the rules can decrypt the ciphertext. In other words, it is an attack that ignores the rules of the system and eventually breaks the security and leaks information. This attack can be more critical for the application environments that require strong security not to leak any related information about ciphertext. In this paper, we demonstrate the possible threat of the Fake Policy Attack by providing two relevant examples. Then, we propose a scheme called Policy Authenticable ABE (PA-ABE) to resolve this issue. We provide a formal security analysis of the proposed scheme and performance evaluation results based on our implementation.

With the advent of advanced technology, the IoT has made possible the connection of numerous devices that can collect vast volumes of data. Hence, the demands of IoT security is paramount. Cryptography is being used to secure the authentication, confidentiality, data integrity and access control of networks. However, due to the many constraints of IoT devices, traditional cryptographic protocols are no longer suited to all IoT environments, such as the smart city. As a result, researchers have been proposing various lightweight cryptographic algorithms and protocols to secure data on IoT networks. This paper discusses state-of-the-art lightweight cryptographic protocols for IoT networks and presents a comparative analysis of popular contemporary ciphers. In doing so, it has classified the most current algorithms into two parts: symmetric and asymmetric lightweight cryptography. Additionally, we evaluate several recently developed block cipher and stream cipher algorithms in terms of their security. In the final section of this paper, we address the changes that need to be made and suggest future research topics.

The Internet-of-Things (IoT) is becoming a revolutionary paradigm, moving towards ubiquity in day to day life and used in several applications such as smart healthcare systems, industry 4.0, critical infrastructure, etc. As with any concept that relies on wireless communication, authentication is of paramount importance when it comes to security considerations. Devices in many IoT applications are severely constrained in terms of computational resources and are thus unable to utilize many modern cryptographic methods for security purposes. Physically-Unclonable-Functions (PUFs) propose to solve this issue by allowing devices to generate unique and secure digital fingerprints at extremely low computational cost. However, PUFs are vulnerable to Machine-Learning-based modeling-attacks that can mathematically clone the PUFs in order to impersonate them. To address these requirements, this paper introduces a new lightweight and practical anonymous authentication protocol for IoT that is resilient against machine learning attacks on PUFs.

Vehicle platoon, a novel vehicle driving paradigm that organizes a group of vehicles in the nose-to-tail structure, has been considered as a potential solution to reduce traffic congestion and increase travel comfort. In such a platoon system, head vehicles' performances are usually evaluated by user vehicles' feedbacks. Selection of an appropriate and reliable head vehicle while not disclosing user vehicles' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving platoon recommendation scheme, called TPPR, to enable potential user vehicles avoid selecting the malicious head vehicles. The basic concept of TPPR is that each user vehicle holds a trust value, and the reputation score of the head vehicle is calculated via a truth discovery process. To preserve vehicles' privacy, pseudonyms and Paillier cryptosystem are applied. In addition, novel authentication protocols are designed to ensure that only the valid vehicles (i.e., the vehicles holding the truthful trust values and joining the vehicle platoon) can pass the authentication. A comprehensive security analysis is conducted to prove that the proposed TPPR scheme is secure against several sophisticated attacks in vehicular ad hoc networks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

Many IoT devices lack the necessary interfaces (keyboards, screens) for entering passwords or changing default ones. For these devices, bootstrapping trust can be challenging. We address the problem of device pairing in the absence of any shared secrets. Pairing is a two-phase process that requires the mutual authentication between the two parties and the agreement to a common key that can be used to further bootstrap essential cryptographic mechanisms. We propose a secret-free and in-band trust establishment protocol that achieves the secure pairing of commercial off-the-shelf (COTS) wireless devices with a hub. As compared to the state-of-the-art, our protocol does not require any hardware/firmware modification to the devices, or any out-of-band channels, but can be applied to any COTS device. Furthermore, our protocol is resistant to active signal manipulations attacks that include recently demonstrated signal nullification at an intended receiver. These security properties are achieved in-band with the assistance of a helper device such as a smartphone and by exploiting hard-to-forge signal propagation laws. We perform extensive theoretical analysis to verify the security of the proposed protocol. In addition, we validate our theoretical results with experiments using COTS devices and USRP radios.

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

With recent developments in communication technologies, vehicular networks have become a reality with various applications. However, the cybersecurity aspect of vehicular networks is still an open issue that needs to be addressed with novel defence mechanisms against attacks. This paper first presents the state-of-The-Art communication technologies in vehicular networks (either inter-vehicle networking or in-vehicle networking) along with their applications. Then we explore novel technologies including machine learning and blockchain as cybersecurity defence mechanisms in vehicular networks. Based on the extensive survey, we highlight some insights for future research to secure vehicular networks.

Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g. cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

SMK Negeri 3 Bengkulu City is one of the vocational schools in Bengkulu City. The school has a major in Computer and Network Engineering (TKJ). Students will be taught about basic knowledge in computers, network infrastructure, network security and others. Cryptography is part of a security system, where cryptography is the science and art of keeping messages secure. Increased security in the form of an encryption process that is carried out through 2 stages before getting the encryption results, namely encryption using the Caesar Cipher Algorithm and then being encrypted again using the Rail Fence Algorithm. To assist the text security system at SMK Negeri 3 Bengkulu City, a client server-based application was built using the Visual Basic .Net programming language with SQL Server database. In its application, one laptop will be used as a database server, so that the results of inputting text that will be sent to the recipient are automatically stored on the server. While the client only accesses the form of sending and receiving messages through the application that has been provided. If you enter an incorrect or incorrect key, then the plaintext cannot be changed to its original form so that the text message cannot be read

With the arrival of the Internet-of-Things era, IoT devices and the services built on them make our lives more convenient and also raise public concerns on their vulnerability to attacks. Recent literature advocates physical-layer solutions to help IoT devices detect attacks instead of using sophisticated cryptographic methods. However, there is still no satisfying solutions for IoT devices with single antenna and sparse traffic. Thus, we introduce SecurePilot to fill this gap. SecurePilot is an unsupervised and plug-and-play solution which works without an attacker’s knowledge in advance. It leverages the strengths of two orthogonal physical-layer information, propagation signatures and device signatures embedded in pilot signals to enable effective attack detection. It could work on single-antenna IoT devices with sparse traffic and also work compatibly with communication protocols. The experimental results show that SecurePilot can successfully detect 99.6% of attacks, triggering false alarms on 3.1% of legitimate traffic in a typical office environment.

In the era of Society 5.0, technological progress is unstoppable. Technological advances in the era of society 5.0 have become the main needs of humans in helping daily work. Technology is used as a medium for finding information to transportation tools that are integrated with the intervention of digital technology. Almost every company has technology to support their business development needs. And especially in agencies in this era, employee data is also processed using technology, but with the integration of employee data into technology, a lot of sensitive data must be protected in order to avoid misuse by irresponsible people. Therefore, the main objective of this research is to develop the application of the Message Diggest Algorithm (MD5) Cryptography Algorithm in employee data applications. This application is very appropriate to make it easier for companies to manage their employee data and ensure data security. To secure sensitive data from each employee, the process carried out is by encrypting each sensitive data so that it is not easy to read by others who have no interest or authority over the data. And the desktop-based application was made using Visual Basic. The research was carried out with two methods, namely the method to test the compilation speed of MD5 and to test the validation of the decrypted data from the MD5. And from the results of the study, the average speed of 500-515 milliseconds and data consistency obtained valid results from all tests.

Quantum cryptography concentrates on the solution of cryptography that is imperishable due to the reason of fortification of secrecy which is applied to the public key distribution of quantum. It is a very prominent technology in which 2 beings can securely communicate along with the sights belongings to quantum physics. However, on basis of classical level cryptography, the used encodes were bits for data. As quantum utilizes the photons or particles polarize ones for encoding the quantized property. This is presented in qubits as a unit. Transmissions depend directly on the inalienable mechanic's law of quantum for security. This paper includes detailed insight into the three most used and appreciated quantum cryptography applications that are providing its domain-wide service in the field of mobile cloud computing. These services are (it) DARPA Network, (ii) IPSEC implementation, and (iii) the twisted light HD implementation along with quantum elements, key distribution, and protocols.