Human Pose Estimation (HPE) is a core component in real-time decision systems, supporting critical applications such as healthcare monitoring, autonomous driving, and sports analytics. While deep learning models—particularly CNNs and Transformer-based architectures—have significantly improved HPE accuracy, they remain vulnerable to adversarial perturbations that subtly distort keypoint localization, thereby undermining system reliability. To address this challenge, we propose regularization and pruning transferable adversarial attack (RPA), a novel framework designed to enhance the transferability of adversarial samples in Transformer-based HPE models. RPA integrates two synergistic strategies: gradient regularization, which suppresses dominant feature correlations to reduce overfitting, and adaptive weight pruning, which removes redundant parameters to reduce model-specific noise. This dual mechanism enables the generation of transferable adversarial attacks that are effective across diverse model architectures. Extensive experiments on state-of-the-art HPE networks demonstrate that RPA consistently outperforms existing attack methods. In white-box settings, RPA reduces average precision (AP) by 0.05-0.30; in black-box scenarios, it yields AP drops of 0.01-0.04. These findings expose critical vulnerabilities in IoT-enabled HPE applications and establish a new benchmark for evaluating adversarial robustness in real-time perception systems.

Reputation-based authentication is a mechanism used to establish trust and ensure the reliability of vehicle communication in vehicular ad hoc networks (VANETs). However, existing schemes ignore the importance of privacy protection and face issues such as malicious vehicles spreading false or duplicate messages. To address these issues, this paper proposes a secure and anonymous reputation-based authentication scheme based on the ElGamal cryptosystem for the vehicular communication system, which promotes cooperative behavior and encourages vehicles' positive contribution to the network. In particular, we design an efficient duplicate detection mechanism based on the technique of Bloom filter to aid in identifying and isolating malicious vehicles. Security analysis is conducted to demonstrate the robust security of the proposed scheme. The results of performance evaluation highlight the superiority of the proposed scheme in addressing the identified shortcomings while maintaining reasonable computation and communication costs.

This paper investigates data communication and access control in satellite-assisted Internet of Things. In particular, given the characteristics of an open communication environment, a multi-layer heterogeneous network, and a time-varying topology in the Space-Air-Ground-Sea Integrated Network (SAGSIN), traditional data communication and security mechanisms built upon the TCP/IP architecture may not fully leverage their potential. Current networks are primarily responsible for end-to-end transmission of binary data, lacking the capability to semantically perceive and handle dynamic and decentralized content. This leads to significant performance gaps in the network. In other words, it is better to retrieve expected information directly from the network and perform content protection on it with low dependency. We propose DARS, a Data Access scheme with Robust and Secure content communication for Satellite-assisted Internet of Things (S-IoT), leveraging the architectural benefits of content centric networks and rich attributes of IoT. DARS enables automatic data retrieval and access control without additional mechanisms, such as online certificate distribution, homogeneous network, and other presuppositions, which facilitates DARS to be applied in various environments. Additionally, DARS integrates a combination of techniques, including semantic representation, cryptographic technologies, and content caching, from a network-centric perspective. Theoretical analysis and experimental simulations show that DARS simplifies system operations and offers a viable solution for satellite-assisted IoT-based applications.

Reputation systems are widely used to provide a trustworthy environment and improve the sustainability of online discussions. They help users understand and evaluate the quality of information by collecting and counting feedback from different users. However, a common issue in most reputation systems is how to maintain users’ reputation and protect their anonymity simultaneously. In this paper, we introduce a new practical anonymous reputation system based on SGX. The establishment of an anonymous reputation system has a positive effect on sustainable trust in reputation-based online applications. Our system achieves the combination of reputation and anonymity by utilizing Intel SGX and the Bloom filter. The Path ORAM algorithm is also implemented to resist side-channel attacks. The experiments demonstrate that our system achieves high performance in terms of computation and storage costs. When compared to two state-of-the-art anonymous reputation systems, our system has better computation performance with at least three orders of magnitude.

Abstract Recommender systems have been widely used for implementing personalised content on many mobile online services to reduce computational overload and preserve wireless data for users. The underlying mechanisms used for building recommender systems analyse data collected from users to make recommendations. This poses concerns over the privacy of data from users as both service providers and the cloud will have access. Privacy-preserving recommender systems protect user information by incorporating various cryptographic mechanisms to prevent accessing the data. However, existing works are not practical due to the use of heavy cryptography. In this paper, we propose an efficient privacy-preserving recommender system that takes advantage of clustering to improve efficiency. Using a secure clustering mechanism, user data are assigned to multiple clusters before being fed into the recommendation. Our proposed protocols are privacy-preserving and do not leak information that could be used to identify a data subject. The experiments show that our system is efficient and accurate.

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

Recommender systems have become ubiquitous in many application domains such as e-commerce and entertainment to recommend items that are interesting to the users. Collaborative Filtering is one of the most widely known techniques for implementing a recommender system, it models user–item interactions using data such as ratings to predict user preferences, which could potentially violate user privacy and expose sensitive data. Although there exist solutions for protecting user data in recommender systems, such as utilising cryptography, they are less practical due to computational overhead. In this paper, we propose RSUC, a privacy-preserving Recommender System based on User Classification. RSUC incorporates homomorphic encryption for better data confidentiality. To mitigate performance issues, RSUC classifies similar users in groups and computes the recommendation in a group while retaining privacy and accuracy. Furthermore, an optimised approach is applied to RSUC to further reduce communication and computational costs using data packing. Security analysis indicates that RSUC is secure under the semi-honest adversary model. Experimental results show that RSUC achieves 4× performance improvement over the standard approach and offers 54× better overall performance over the existing solution.

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 significant attentions in recent years. With the increasing numbers of the connected devices in the 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 the IoT system and the resource-constrained devices, how to efficiently manage such system and guarantee the security and privacy for devices is concerned. In this article, 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.

Establishing trust in the election outcome is a very important problem. In order to solve it, we propose a practical ranked choice online voting system where every user can verify the validity of the outcomes. The proposed system combines the use of public blockchain and Intel Software Guard Extensions (SGX) to ensure that all standard requirements for voting systems are met, and at the same time to provide protection against malicious adversaries with administrative access. Each cast vote is submitted to the Intel SGX via a secure communication channel. The SGX verifies the eligibility and validity of the vote and encrypts it inside the SGX enclave. Later on, only the fingerprints (i.e., the hash values) of the encrypted votes are published on the public blockchain ledger before the deadline of the election. All encrypted votes are released after the deadline. Every voter can self-tally all submissions in encrypted form using the homomorphic property of our encryption system. The proposed protocol also achieves end-to-end voter verification. Our theoretical security analysis and experimental results show that the protocol is secure and is more efficient than previous alternative voting protocols.

Summary Anonymous authentication technique receives wide attention in recent years since it can protect users' privacy. Anonymous web browsing refers to utilization of the World Wide Web that hides a user's personally identifiable information from the websites visited. Even if a user can hide the IP address and other physical information with anonymity programs such as Tor, the web server can always monitor the user on the basis of the identity. In this paper, we firstly give an overview and cryptanalysis on the protocol of Yang et al and point out the security weaknesses of their protocol. Then, we propose a new authentication protocol for anonymous web browsing. In the proposed protocol, we take the advantages of a pseudo identity mechanism and an identity‐based elliptic curve cryptography algorithm to achieve user anonymity, robust security, and high efficiency. The result of security analysis and performance evaluation indicate the feasibility and practicality of our proposed anonymous authentication protocol.

Touch-based fingerprint sensor has been used in the mobile device, which can easily be hacked by an individual "fake fingerprint". Fingerprint captured by mobile camera is an alternative for identity authentication to control the access to the mobile device. However, the touch-less fingerprint needs to be captured under strictly controlled environment because only front view finger image presents useful minutiae information. It is very difficult for a user to capture an exactly front view image in reality. A mobile fingerprinting scheme for identity authentication is proposed in this paper. Multiple view fingerprint images are captured by mobile camera. A method for selecting the best candidate as the front view image is developed. Experimental result shows the effectiveness of the proposed method.