Electronic voting systems play an important role in modern democratic governance and smart city services supported by the Internet of Things (IoT). However, ensuring privacy and verifiability effectively remains a considerable challenge, especially for identifiable ballot (B-ID) schemes. To address this issue, this paper introduces Evosmt, a novel verifiable privacy-preserving e-voting scheme. As far as we know, this is the first scheme aiming to safeguard voters’ privacy in the presence of a potentially malicious tallier without trusting multiple authorities. Evosmt uses bit-XOR encryption to provide compact 0/1 ciphertext proofs and adopts an optimized authenticated garbled circuit (GC) with almost universal linear hash functions for efficient tallying and verification. A security analysis of the scheme has been conducted to demonstrate its correctness, privacy preservation, and verifiability. Through experimental evaluation and comparison with classic authenticated garbled circuit schemes, the proposed scheme has shown more than 100× improvement in terms of setup speed and communication efficiency of each voter when the number of voters exceeds 1000. Compared with classic e-voting schemes, the proposed scheme exhibits negligible encryption and proof cost and much faster verification due to the optimized authenticated garbled circuit method.Thanks to its lightweight and efficient design, the scheme is highly suitable for large-scale IoT-enabled e-voting applications in smart city governance and distributed democratic services.

The construction of the shortest addition chain for a given exponent n, which yields the most efficient method for computing xnin certain multiplicative groups, is vital for enhancing the performance of modern cryptography. Cryptosystems such as RSA, ElGamal, Paillier, and Elliptic Curve Cryptography (ECC) all rely on fast exponentiation and scalar multiplication, where addition chains play a key role. However, finding the shortest addition chain is known to be an NP-Complete problem. Moreover, existing algorithms struggle to generate short addition chains efficiently for large integers. In this paper, we integrate genetic algorithms with the window method to develop an effective strategy for computing short addition chains for large integers. We conduct experiments using an RSA-1536 modulus to demonstrate the efficiency and practicality of our proposed algorithm.

The development of the Internet of Things (IoT) has led to the widespread use of WiFi-enabled consumer electronic devices, which are now common in everyday life. These advancements in IoT have greatly improved data collection and analysis capabilities, especially for health monitoring applications. However, traditional centralized machine learning methods often fall short, raising significant privacy concerns and requiring extensive data collection, which is inefficient. To address these limitations within the distributed IoT environment, this article presents a federated learning (FL)-based WiFi sensing system specifically designed for health monitoring. By enabling local model training, our system prevents the sharing of sensitive data, thus reducing the risk of privacy breaches. We further enhance our system with a secret sharing mechanism coupled with model sparsification to significantly improve privacy. Additionally, our improved top-k model sparsification algorithm, equipped with adaptive residuals, reduces communication overhead while ensuring high accuracy. Extensive testing across various datasets and models confirms that our system outperforms existing benchmarks in terms of privacy protection and communication efficiency, marking a substantial advancement in health monitoring within the IoT.

Federated learning (FL) is an emerging paradigm in edge computing that facilitates the training of machine learning models across numerous resource-constrained edge devices without the necessity of transferring data to a centralized server. A significant challenge in federated learning is the statistical heterogeneity of the client data distribution, which can severely undermine the generalizability and performance of the models in individual clients, particularly in the context of the Internet of Things (IoT). In this paper, we observe that the initialization process in each communication round results in the loss of personalized knowledge on the clients, which is closely related to the above challenges. Based on this observation, we propose a novel personalized federated learning framework, FedHKD-SA, which enables clients to store and refine the knowledge from historical knowledge models in each round of local model training, accelerating the recall of personalized knowledge for the most recently initialized client models. Moreover, the second aggregation on the server side facilitates targeted learning of the localized personalized knowledge conveyed by local models, skillfully maintaining a balance between generalization and personalization, and accelerating model convergence. Extensive experiments across various datasets and settings demonstrate the effectiveness and robustness of our framework.

Mobile smart devices provide convenience for people’s daily life with the users’ data, but also put consumers’ privacy and security at risk. Privacy-enhancing technologies (PETs), including secure two/multi-party computation, have emerged as solutions to alleviate privacy concerns in mobile cloud computing (MCC). However, cloud servers, although capable of easing the burden of PETs, introduce potential risks by being malicious and colluding with computation parties to access additional private data. In this article, we propose a privacy-preserving cloud-assisted two-party computation scheme and the optimized variant with the half-gate method in MCC with a higher security level. To the best of our knowledge, the work is the first cloud-assisted two-party computation, designed to resist all collusion attacks in the malicious model. This is achieved by distributing circuit generation tasks among the parties and separately processing private inputs based on authenticated garbled circuits. Security analysis demonstrates that our scheme ensures correctness and fairness. Performance comparison results indicate the efficiency of our work, even with stronger security against malicious servers and any collusion attack. It outperforms the state-of-the-art scheme, particularly in terms of the server’s communication cost in the online phase, achieving a remarkable reduction of approximately 96.8%.

Prevailing smart devices collect individual or industrial sensitive data for collaborative computation to provide convenient service in heterogeneous networks. Nowadays, protecting privacy and security is a significant issue and raises increasing concerns in academia and industry. But diverse smart devices are equipped with unequal resources and some devices with limited resources cannot afford expensive privacy-preserving computation. In this article, we propose a generic efficient and privacy-preserving cloud-assisted two-party computation scheme for smart devices in heterogeneous networks. We adopt the cloud server to assist the collaborative computation and reduce the overhead of smart devices. Besides, we apply preprocessing and online phases to guarantee different devices to operate with a lower burden online. What is more, the work is, to our best knowledge, the first to resist the malicious cloud server and computing parties simultaneously by adopting authenticated masked bits to strengthen the garbled circuit scheme. At the same time, our scheme can guarantee correctness and fairness, as shown in security analysis. The performance comparison result shows that this work is efficient and surpasses the previous best counterpart scheme while maintaining nearly identical computation cost. It outperforms in terms of total communication cost by 49% and total execution time by 32%, even though it takes extra and acceptable cost in the online phase for stronger security against the malicious server.

Smart grids bring advantages of reliability and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and security in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the article. First, we adopt a Chinese Remainder Theorem (CRT) to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center (CC). Second, our scheme is efficient for encryption without high-cost additive homomorphic public-key encryption (PKE) scheme, such as the Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computational complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity, and data integrity of the users’ reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than the existing competing approaches in terms of computational and communication overheads.