The widespread adoption of data-driven services, including networked data collection and analysis systems, has greatly enhanced convenience and decision-making, but it has also raised growing concerns about the trade-off between fine-grained utility and personalized privacy guarantees. Personalized Differential Privacy (PDP) offers a flexible framework by allowing users to specify individualized privacy budgets. However, existing sampling-based PDP mechanisms often rely on coarse risk modeling assumptions that treat individual data characteristics uniformly, leading to suboptimal utility and inefficient privacy expenditure. In this paper, we propose the Utility-Aware Sampling Mechanism (UASM), a principled PDP implementation that enables fine-grained, user-centric privacy control while explicitly optimizing utility. First, UASM formalizes policy-assisted secret specifications, allowing confidentiality to be determined through a combination of baseline protection rules and personalized privacy preferences, and combines them with individualized privacy budgets. Second, UASM employs a two-stage utility-aware sampling strategy to calibrate noise: (i) an optimal global threshold selected to reduce unnecessary privacy-budget wastage while respecting users’ declared budgets, and (ii) a sensitivity-aware refinement stage that allocates privacy loss according to each record’s influence on query accuracy. Formal privacy analysis demonstrates that UASM provides rigorous privacy guarantees and promotes fairer privacy expenditure under heterogeneous privacy requirements. Extensive experiments on synthetic and real-world datasets, including network-oriented downstream tasks, show that UASM achieves a superior privacy-utility trade-off over state-of-the-art PDP baselines, underscoring its practical effectiveness.

The exponential growth of wearable medical devices (WMDs) and the increasing demand for real-time health data sharing necessitate secure and fine-grained access control mechanisms. However, existing ciphertext-policy attribute-based encryption (CP-ABE) schemes suffer from computational and storage overheads that grow linearly with policy complexity. To address this challenge, we propose fog-assisted composite attribute-based encryption (FA-CABE), a novel scheme that integrates composite attributes with fog computing to enhance efficiency. FA-CABE leverages the subset sum problem (SSP) to map conjunctive policy clauses to composite attributes, substantially reducing both encryption and decryption overhead. A dualfog-node architecture offloads cryptographic computations from WMDs, enabling lightweight local processing. Rigorous security analysis under the Decisional Bilinear Diffie-Hellman (DBDH) assumption demonstrates that FA-CABE achieves replayable chosen ciphertext attack (RCCA) security. Experimental results show that FA-CABE achieves encryption speeds that are 22.13×–145.02× faster and decryption speeds that are 6.71×–161.83× faster than existing schemes, while requiring only a constant number of operations for decryption. Additionally, experimental validation on the Raspberry Pi 4B shows that the energy consumption is as low as 0.72 W per core, with data processing speed reaching 23.38 MB/s.

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user’s personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model’s feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation.

Physical layer security has attracted significant attention in the field of wireless communications. The application of artificial noise can reduce the eavesdropping ability of illegal eavesdroppers without affecting legitimate users. However, most current physical layer security schemes only consider static scenarios and do not account for mobility. Some schemes analyze security performance in mobile scenarios with friendly jammers but do not consider the handoff and cooperation of friendly jammers due to mobility. To address this challenge, we propose a scheme for soft-handoff-based cooperative jamming (CJSH) in mobility scenarios. Initially, we consider a common scenario where a base station communicates with a legitimate mobile user, alongside a mobile passive eavesdropper and multiple friendly jammers emitting artificial noise in the circular area covered by the base station’s signal. Next, We measure the connection outage probability (COP) and secrecy outage probability (SOP) under the influence of multiple friendly jammers in the proposed scheme. We also design two corresponding thresholds for jammers to join and exit. To balance security and energy consumption, we define the Power Average Security Gain (PASG) as a measure of system performance. Finally, we provide numerical simulation results to verify the rationality of the proposed handoff scheme, demonstrating its effective improvement of the system’s security performance and power utilization.

Data aggregation plays a crucial role in smart grid communication as it enables the collection of data in an energy-efficient manner. However, the widespread deployment of smart meters has raised significant concerns regarding the privacy of users' personal data. Therefore, in this paper, we present an efficient and privacy-preserving data aggregation and trust management scheme (PATM) for an IoT-enabled smart grid based on smart contract. Firstly, we propose a five-layer architecture for smart grid communication to support secure and efficient data aggregation and management. Under the architecture, the Boneh-Goh-Nissim cryptosystem with blind factor is improved to facilitate privacy protection. In addition, the tamper-evident nature of blockchain is utilized for effective data management. Our designs also enhance the resistance to differential attack and prevent privacy breaches during the aggregation process. Detailed security proof and theoretical analysis confirm that our PATM can satisfies the necessary security and privacy requirements while maintaining the required efficiency for smart grid operations. Furthermore, comparative experiments demonstrate that PATM outperforms other proposed work in terms of storage cost, computational complexity, and utility of differential privacy.

With the rapid development of the smart grid (SG), the security and reliability of communication for SG has become an urgent and critical issue. The authentication and key agreement (AKA) protocol has emerged as important means to address the security challenges in smart grid. This paper proposes a novel certificateless authentication and key agreement protocol (NCL-AKA) for the smart grid, which overcomes the limitations of existing AKA protocol such as complex certificate management, untimely certificate revocation, and man-in-the-middle attacks. Firstly, the system architecture and threat model of the proposed protocol is presented. Then, the correctness and security of the proposed protocol are analyzed. Finally, comparative experiments and analysis reveal that the NCL-AKA has lower computational cost and communication overhead.

As one of the most promising machine learning frameworks emerging in recent years, Federated learning (FL) has received lots of attention. The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally. However, recent studies have shown that traditional centralized federated learning is vulnerable to various attacks, such as gradient attacks, where a malicious server collects local model gradients and uses them to recover the private data stored on the client. In this paper, we propose a DEcentralized FEderated learning Against aTtacks (DEFEAT) framework and use it to defend the gradient attack. The decentralized structure adopted by this paper uses a peer-to-peer network to transmit, aggregate, and update local models. In DEFEAT, the participating clients only need to communicate with their single-hop neighbors to learn the global model, in which the model accuracy and communication cost during the training process of DEFEAT are well balanced. Through a series of experiments and detailed case studies on real datasets, we evauate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.

In vehicularad hocnetworks (VANETs), road safety and road traffic efficiency can be improved through message interaction between vehicle users, which inevitably relies on secure identity authentication, and message credibility verification. Existing authentication and message verification mechanisms are prone to severe single points of failure and low authentication efficiency due to their reliance on the trusted third party, especially during the user registration phase. This article proposes a double-layer blockchain and decentralized identifiers assisted secure registration and authentication (BDRA) mechanism for decentralized VANETs, which can achieve the following advantages: 1) realizing a secure and decentralized user registration phase by using the decentralized identifier (DID) technology; 2) accomplishing efficient authentication and message verification by combining double-layer blockchain, DIDs and a reputation feedback strategy; and 3) enabling a more efficient cross section reregistration that reduces the communication time by 30%. The security features and efficiency of the BDRA mechanism are demonstrated by carrying out security analysis and performance evaluation, which is based on the hyperledger fabric (HLF) platform.

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.

Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework where the FL clients are also the blockchain miners, clients have to train the local models, broadcast the trained model updates to the blockchain network, and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources to training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to help the model owner (MO) (i.e., the BCFL task publisher) assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the MO and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks , and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption , and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

The boom of the Internet of Things (IoT) has exposed many security issues in recent years. Cooperative jamming, including the continuous jamming strategy (CJS) and intermittent jamming strategy (IJS), is an effective approach toward secure wireless communication in the physical layer. CJS used to be a primary physical-layer security technology that sends cooperative jamming signals at the expense of energy consumption. Different from CJS, IJS is more energy efficient. The feasibility of IJS has been proved in a slotted scenario, which motivates us to design IJS in a nonslotted scenario. In this article, we discuss the feasibility of IJS for a nonslotted transmission IoT system and formulate an optimization problem based on a sense-harvest-jam policy. This problem is to find the optimal matching precision between durations of artificial noise and legitimate signals. To solve this problem, we exploit a backpropagation-neural-network model to analyze jamming duration proportion and derive the optimal proportion for the binary phase-shift keying modulation. Finally, we design a matching precision optimization algorithm to achieve the optimal nonslotted secure transmission. Simulation results on jamming efficiency demonstrate that the proposed IJS has preferable secure performance than the CJS under energy constraints.

Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.

We define the following problem termed n × 1-out-of-n oblivious transfer (n × 1-out-of-n OT): in a system with one server and n clients, how to securely and efficiently assign n secrets to n clients by the server, with each client getting a unique secret from the server, and the server and clients remain unknown of how the secrets are distributed? This is a novel problem that is fundamentally different than 1-out-of-n OT repeated n times, and is different than k-out-of-n OT as well. Nevertheless, the proposed OT has many practical applications such as privacy-preserving data aggregation in smart grids. It can also be employed to design crypto protocols for anonymous communications and group signatures. In this paper, we propose the first algorithm to efficiently and effectively implement the n × 1-out-of-n OT. We construct hidden permutation circuits to obliviously assign n secrets to n clients by the server within O(lg(n)) time. A rigorous theoretical analysis is also carried out to investigate the security strength and performance of the protocol.

With the dramatically increasing deployment of IoT devices, storing and protecting the large volume of IoT data has become a significant issue. Traditional cloud-based IoT structures impose extremely high computation and storage demands on the cloud servers. Meanwhile, the strong dependencies on the centralized servers bring significant trust issues. To mitigate these problems, we propose a distributed data storage scheme employing blockchain and cetrificateless cryptography. Our scheme eliminates the traditional centralized servers by leveraging the blockchain miners who perform “transaction” verifications and records audit with the help of certificateless cryptography. We present a clear definition of the transactions in a non-cryptocurrency system and illustrate how the transactions are processed. To the best of our knowledge, this is the first work designing a secure and accountable IoT storage system using blockchain. Additionally, we extend our scheme to enable data trading and elaborate how data trading can be efficiently and effectively achieved.

The Internet of Things (IoT) is changing human lives by connecting everyday objects together. For example, in a grocery store, all items can be connected with each other, forming a smart shopping system. In such an IoT system, an inexpensive radio frequency identification (RFID) tag can be attached to each product which, when placed into a smart shopping cart, can be automatically read by a cart equipped with an RFID reader. As a result, billing can be conducted from the shopping cart itself, preventing customers from waiting in a long queue at checkout. Additionally, smart shelving can be added into this system, equipped with RFID readers, and can monitor stock, perhaps also updating a central server. Another benefit of this kind of system is that inventory management becomes much easier, as all items can be automatically read by an RFID reader instead of manually scanned by a laborer. To validate the feasibility of such a system, in this paper we identify the design requirements of a smart shopping system, build a prototype system to test functionality, and design a secure communication protocol to make the system practical. To the best of our knowledge, this is the first time a smart shopping system is proposed with security under consideration.

Ultraviolet (UV) radiation has a great impact on human health. Nowadays, the public basically gets information about UV radiation through weather forecasts, which can only provide rough and average prediction for a certain large area. Since CMOS sensors in smartphone cameras are very sensitive to UV radiation, smartphones have potential to be the ideal equipment to measure it. At the same time, result optimization can be achieved in real time by taking advantage of fog computing because fog servers are able to aggregate UV radiation data and compute the results at local areas. This paper exhaustively discussed a novel procedure that could measure UV radiation through smartphone cameras, and also briefly covered how to leverage fog computing to improve UV measurement accuracy. To implement the procedure, an Android app called UV meter was developed. Experiments were conducted by utilizing the app to validate and evaluate the correctness and accuracy of the procedure on both smartphones and smart watches. Results showed that the proposed procedure could achieve an average of 95% accuracy of a typical professional digital UV meter, and could be easily implemented on smart devices.

The development of the Internet of Things has made extraordinary progress in recent years in both academic and industrial fields. There are quite a few smart home systems (SHSs) that have been developed by major companies to achieve home automation. However, the nature of smart homes inevitably raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving communication protocol for the SHSs. In our proposed scheme, data transmissions within the SHS are secured by a symmetric encryption scheme with secret keys being generated by chaotic systems. Meanwhile, we incorporate message authentication codes to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead.

Due to the popularity of auction mechanisms in real-world applications and the increasing awareness of securing private information, auctions are in dire need of bid-privacy protection. In this paper, we design three secure, multi-unit, sealed-bid, first-price auction schemes. The first is a secure auction using homomorphic encryption and is denoted by SAHE; the second is a secure action using masking values and is denoted by SAMV; and the third has an improved masked noise algorithm, denoted by ISAMV. In the first, SAHE, the auction is processed on encrypted bids by a server, and the final output is only known by the auctioneer. Neither the auctioneer nor the server can obtain the full information of the bidders. The second and third auctions, SAMV and ISAMV, decrease computational complexity. Instead of homomorphic encryption, they use random noise to mask the bid values. By using a masking method, the server only knows the noise, and the auctioneer only knows the auction results; neither will see the private information of the bidders. All three schemes enable the auctioneer to verify that the winners have paid the correct amounts. A thorough theoretical analysis is performed to evaluate the security properties, computational complexity, and communication complexity of the auctions. Copyright © 2016 John Wiley & Sons, Ltd.