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In this article, we focus on the concept of locally-APN-ness (``APN" is the abbreviation of the well-known notion of Almost Perfect Nonlinear) introduced by Blondeau, Canteaut, and Charpin, which makes the corpus of S-boxes somehow larger regarding their differential uniformity and, therefore, possibly, more suitable candidates against the differential attack (or their variants). Specifically, given two coprime positive integers $m$ and $k$ such that $\gcd(2^m+1,2^k+1)=1$, we investigate the locally-APN-ness property of an infinite family of Niho type power functions in the form $F(x)=x^{s(2^m-1)+1}$ over the finite field ${\mathbb F}_{2^{2m}}$ for $s=(2^k+1)^{-1}$, where $(2^k+1)^{-1}$ denotes the multiplicative inverse modulo $2^m+1$. By employing finer studies of the number of solutions of certain equations over finite fields (with even characteristic) as well as some subtle manipulations of solving some equations, we prove that $F(x)$ is locally APN and determine its differential spectrum. It is worth noting that computer experiments show that this class of locally-APN power functions covers all Niho type locally-APN power functions for $2\leq m\leq10$. In addition, we also determine the boomerang spectrum of $F(x)$ by using its differential spectrum, which particularly generalizes a recent result by Yan, Zhang, and Li.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes in WSN are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharing and privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Identity-based cryptography is proposed to solve the complicated certificate management of traditional public-key cryptography. The pairing computation and high-level tower extension field arithmetic turn out to be the performance bottleneck of pairing-based signature schemes. Graphics processing units have been increasingly popular for general-purpose computing in recent years. They have shown a lot of promise in speeding up cryptographic schemes such as AES, RSA, and ECDSA. However, to our knowledge, the research on parallel implementation of pairings and identity-based cryptographic schemes on graphics processing units is somewhat outdated. Therefore, in this article, we implement the identity-based signature scheme in the IEEE P1363 Standard on a modern NVIDIA RTX 3060 card. We convert the pairing computation in signature verification into a product of pairings with fixed arguments and therefore avoid the scalar multiplication in 𝔾 2 . Then we employ the precomputation technique to improve the elliptic curve scalar multiplication, exponentiation in \(\mathbb {F}_{p^{12}}\) and the pairing computation. We also apply PTX ISA to multiple-precision arithmetic. Experiments demonstrate that our implementation can perform 43,856/46,753/39,798 pairings/sec for the Optimal Ate pairing, the pairing with a fixed argument, and two pairings with fixed arguments, respectively. Peak throughputs of signature generation and verification can achieve 322.6 and 40.6 kops/sec over the BN254 curve.

Outsourcing data to cloud services is a good solution for users with limited computing resources. Privacy and confidentiality of data is jeopardized when data is transferred and shared in the cloud. The development of searchable cryptography offers the possibility to solve these problems. Symmetric searchable encryption (SSE) is popular among researchers because it is efficient and secure. SSE often requires the data sender and data receiver to use the same key to generate key ciphertext and trapdoor, which will obviously cause the problem of key management. Searchable encryption based on public key can simplify the key management problem. A public key encryption scheme with keyword search (PEKS) allows multiple senders to encrypt keywords under the receiver’s public key. It is vulnerable to keyword guessing attacks (KGA) due to the small size of the keywords. The proposal of public key authenticated encryption with keyword search (PAEKS) is mainly to resist inside keyword guessing attacks. The previous security models do not involve the indistinguishability of the same keywords ( w 0 × × = w 1 ), which brings the user’s search pattern easy to leak. The essential reason is that the trapdoor generation algorithm is deterministic. At the same time, most of the existing schemes use bilinear pair design, which greatly reduces the efficiency of the scheme. To address these problems, the paper introduces an improved PAEKS model. We design a lightweight public key authentication encryption scheme based on the Diffie-Hellman protocol. Then, we prove the ciphertext indistinguishability security and trapdoor indistinguishability security of the scheme in the improved security model. Finally, the paper demonstrates its comparable security and computational efficiency by comparing it with previous PAEKS schemes. Meanwhile, we conduct an experimental evaluation based on the cryptographic library. Experimental results show that the computational overhead of our scheme compared with the ciphertext generation algorithm, trapdoor generation algorithm and test algorithm of other schemes Our scheme reduces 274, 158 and 60 times, respectively.

The aggregate signature is a special signature mode widely used in the wireless medical sensor network. The aggregate signature can reduce bandwidth and storage space in this sensor network by aggregating multiple signatures from different signers into a short signature. Traditional aggregate signature protocols based on the discrete logarithm problem and the great integer factorization problem cannot resist quantum attacks. In this article, we propose a new noninteractive identity-based aggregate signature protocol from lattices. We prove that the proposed protocol is secure in the quantum random oracle model. Additionally, the performance of the proposed protocol is much better than most previous protocols according to our experiment. Meanwhile, the size of the aggregate signature in our protocol is a logarithmic function of the number of signatures being aggregated.

Ensuring the security and privacy of users and data in a mobile edge computing (MEC) deployment, without affecting performance, latency and and user quality of experience remains challenging. For example, in this paper we revisit an identity-based anonymous authentication scheme designed for MEC deployment. Then, we reveal that the scheme is vulnerable to impersonation, replay and denial of service (DoS) attacks, contrary to their claims. It also does not achieve user un-traceability, and the registration center must be online during authentication. We also observe that it is unclear from their scheme description, what encryption algorithm should be used in the authentication process. Therefore, we redesign the scheme in order to mitigate the weaknesses pointed out. Our redesigned protocol uses password and biometrics for authentication, which broadens the scope for real-world implementation. We also provide both formal security proof and heuristic security analysis to demonstrate that the proposed scheme achieves the desired security goals. A performance comparison shows that our scheme outperforms four other competing schemes in terms of computation and communication costs.

Covert communication is designed for hiding the subliminal communication which takes place between both of the speakers and their relationship. The traditional covert communication utilizes the centralized channel and the third-party central node or authority to distribute messages which leads to a lack of undetectability, antitraceability, and robustness. In recent years, there have been attempts to apply the blockchain to covert communication solutions, for the characteristics of blockchain such as decentralization, openness, and trustworthiness. In this paper, based on the analysis of the literature and the classification according to the hiding position, we identify four kinds of covert communication: Address Channel, Value Channel, DSA (Digital Signature Algorithm) Channel, and Script Channel, which will help inform future research agenda.

Ensuring the security of cloud storage services is an ongoing challenge, due to the increasingly complex and dynamic threat landscape. One particular challenge is how to design security and efficient data encryption approaches. For example, the key used to encrypt data is generally a string of long random number, and this key is uploaded to the cloud server for security. The threshold password authentication secret sharing (TPASS) protocol is commonly used to save encryption keys. However, existing TPASS protocols may not fully achieve the desired security and efficiency levels due to the complexity of the protocol design. Hence, in this article, we propose an efficient dual-server secret sharing protocol (DssP) based on password authentication for cloud storage services. In our approach, users only need to remember a simple password to achieve random key access through dual server cooperation. We then mathematically prove that DssP meets the security requirements, and also evaluate its performance to show that it has less computational and communication overheads than other similar protocols.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user’s electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant’s privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

Over the past decade, the Internet of Things (IoT) is widely adopted in various domains, including education, commerce, government, and healthcare. There are also many IoT based applications drawn significantly attentions in recent years. With the increasing numbers of the connected devices in IoT system, one of the challenging tasks is to ensure devices’ authenticity which allows users to have a high confidence in the decision. In addition, due to the heterogeneity of IoT system and the resource-constrained devices, how to efficiently manage such system and guarantee the security and privacy for devices are concerned. In this paper, we proposed a new blockchain-based authentication scheme to meet the challenges. Our proposed framework combines the blockchain technique and the modular square root algorithm to achieve an effective authentication process. Besides, we demonstrate the security and utility of the proposed scheme by providing the security analysis and the detailed experiment.

Credit system is generally associated with the banking and financial institutions, although it has far reaching implications for residents of countries, such as U.S., particularly for those with a poor credit history. Specifically, a credit score computation (CSC) quantifies an individual’s credit value or credit risk, which is used by banking and financial institutions, as well as other entities (e.g., during purchasing of insurance policies and application of rental properties), to facilitate their decision-making (e.g., whether to approve the insurance policy purchase or the level of premium). Although a number of CSC models have been proposed in the literature for supporting different application scenarios, privacy protection of CSC is rarely considered despite the potential for leakage of user private information (e.g., registration, hobbies, credit, relationships, and inquiry). Such information can then be abused for other nefarious activities, such as identity theft and credit card fraud. Thus, in this article, we first analyze the privacy strength of existing CSC models, prior to presenting the formal definition of a privacy-preserving CSC system alongside its security requirements. Then, we propose a concrete construction based on Paillier encryption with three proposed noninteractive zero-knowledge schemes. To demonstrate feasibility of our proposal, we evaluate both its security and performance.

In Wireless Sensor Networks (WSNs), data aggregation (DA) has become one of the most practical techniques to reduce processing delay and improve energy efficiency. To support intelligent applications, sensor nodes need to report heterogeneous and diverse data, which induces the demand for multi-dimensional data aggregation and multi-functional data analysis. To solve the current security problems and functional requirements, we propose a multi-functional and multi-dimensional secure data aggregation scheme to strike the balance between data availability and privacy. Firstly, we design a Chinese remainder theorem conversion method with the counter to encode multi-dimensional data into large integers, which can be operated by linear homomorphic encryption schemes. Then, we introduce a multi-functional data analysis method supporting diversified aggregation functions, including linear, polynomial and continuous functions. Moreover, we demonstrate that the proposed scheme can achieve confidentiality, integrity, authentication and resistance against false data injection attacks. Experimental results show that the supported max dimension of one ciphertext in our scheme is at least twice that of existing schemes. Thus, in scenarios with high dimensions, our scheme is superior to the existing schemes in terms of computation and communication costs.

Nowadays, artificial intelligence (AI) has become the core technology for numerous application fields ranging from self-driving cars to smart cities. Smart healthcare, as an important part of smart cities, constitutes one of the most essential pillars of social and economic stability. Despite all the possibilities offered by smart healthcare, how to handle the dark aspects of smart healthcare such as security, privacy and trust issues, and so on remains unsolved. In this paper, we focus on designing a human-in-the-loop-aided (HitL-aided) scheme to preserve privacy in smart healthcare. On the one hand, a block design technique is employed to obfuscate various health indicators from the hospitals and the smart wearable devices. On the other hand, human-in-the-loop (HitL) is introduced to enable a privacy access of the health reports from the smart healthcare platform. In addition, the performance analysis and case study indicate that the proposed HitL-aided scheme is effective in preserving privacy for smart healthcare.

In cloud storage, how to search sensitive data efficiently and securely is a challenging problem. The searchable encryption technique provides a secure storage method without loss of data confidentiality and utilization. As an important branch of searchable encryption, public-key encryption with keyword search (PEKS) is widely studied by scholars. However, most of the traditional PEKS schemes are vulnerable to the inside keyword guessing attack (IKGA) or some other weaknesses. Resisting the inside keyword guessing attack is likely to become a must-have property of all new PEKS schemes. For a long time, mitigating IKGA has been inefficient and difficult, and thus most existing PEKS schemes fail in achieving their security goals. To improve the security and efficiency of PEKS, we define the notion of Dual-server Public-key Authenticated Encryption with Keyword Search (DPAEKS), which protects against IKGA with high computation efficiency, and supports the authentication property. Then, we provide a construction of DPAEKS without bilinear pairings, which is secure against IKGA by leveraging two servers that do not cooperate. Experimental results obtained using a real-world dataset show that our scheme is highly efficient and provides strong security, making it suitable for deployment in practical applications.

Internet of Things (IoT) enables terminal devices connecting with the internet and provides various intelligent applications by analyzing devices data. As a typical IoT technique, edge computing provides a three-tier architecture to reduce communications and improve efficiency. Specifically, edge nodes are responsible for collecting and aggregating device data, and then send processed results to the cloud for subsequent analysis. However, the data aggregation function will compromise the privacy of device data. In this paper, we proposed an efficient privacy-preserving multi-dimensional data aggregation scheme for IoT, called PMDA. The scheme uses the Chinese remainder theorem to design a homomorphic encryption method that encryptes a multiple-dimensional small integer vector into one ciphertext and keeps linear homomorphic properties per dimension. Combining with the signature mechanism and the batch verification method, the scheme guarantees non-repudiation of device data and enhance verification efficiency at edge nodes. Through theoretical analysis, we demonstrate that the proposed scheme can achieve correctness, privacy, authentication, and integrity. After performance evaluation, we demonstrate that our scheme is superior to other schemes in terms of computation and communication costs. In particular, as the message dimension increases, our scheme computation costs almost a tenth of others at the 80-bits security level.