We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for

Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive

In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and

Current deep learning-based steganalyzers often depend on specific image dimensions, leading to inevitable adjustments in network structure when dealing with varied image sizes. This impedes their effectiveness in managing the wide range of image sizes commonly found on social media. To address this issue, our paper presents a novel steganalytic network that is optimized for fixed-size (notably, $256\times

Patients with dysarthria and physical impairments face challenges with traditional user interfaces. An Automatic Speaker Verification (ASV) system can enhance accessibility by replacing complex authentication methods and enabling voice biometrics in various applications for patients with dysarthria. This study focuses on enhancing accessibility of patients with dysarthria through an ASV system. In

We study the problem of distributed multi-user secret sharing (DMUSS), involving a main node, N storage nodes, and K users. Every user has access to the contents of a certain subset of storage nodes and wants to decode an independent secret message. With knowledge of K secret messages, the main node strategically places encoded shares in the storage nodes, ensuring two crucial conditions: (i) each

In recent years, many cloud service providers adopt the pay-per-query model to offer paid search services to the public. The data owner rents the resources of cloud service providers and charges the data user a fee based on the data volume to be queried. While this commercial model offers flexibility, convenience, and cost-effectiveness, it comes with a significant vulnerability to data breaches. Public-key

In recent years, phishing scams have emerged as one of the most serious crimes on Ethereum. Existing phishing scam detection methods typically model public transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis.

Electromagnetic side-channel analysis (EM SCA) attack poses a serious threat to integrated circuits (ICs), necessitating timely vulnerability detection before deployment to enhance EM side-channel security. Various EM simulation methods have emerged for analyzing EM side-channel leakage, providing sufficiently accurate results. However, these simulator-based methods still face two principal challenges

Graph matching over two given graphs is a well-established method for re-identifying obscured node labels within an anonymous graph by matching the corresponding nodes in a reference graph. This paper studies a new application, termed the graph-signal-to-graph matching (GS2GM) problem, where the attacker observes a set of filtered graph signals originating from a hidden graph. These signals are generated

How to enable fairness for e-commerce applications has attracted years of research. Recent research has proposed employing blockchain smart contract as an efficient trusted third party (TTP) to enable fair data trading. However, the state-of-the-art schemes suffer from two issues, i.e., they either fail to work for situations where data validity cannot be encoded as an oracle function in the smart

Specific emitter identification (SEI) is important to guarantee the security of device administration. Recently, to increase the effectiveness of the recognition, traditional SEI employing only one sensor has been extended to the scenario with multiple sensors. However, the inherent distortion at different sensors impacts the radio frequency fingerprints (RFFs) of the emitter independently, which inevitably

The quality of medical services is improved by sharing electronic medical records (EMRs) across multiple medical institutions via cloud edge. However, EMRs contain private information about patients, and cloud servers are untrustworthy, thus they cannot be shared arbitrarily among senders and receivers. Access control encryption (ACE) is a preferred technique that produces encrypted EMRs and then restricts

Specific emitter identification (SEI) plays a crucial role in the security of the Industrial Internet of Things (IIoT). In recent years, research on applying deep learning (DL) methods for signal identification has mushroomed. However, DL-based SEI methods rely on a huge amount of training data and powerful computing devices, limiting their application scenarios. In addition, DL models are considered

This work proposes an On-Off Keying (OOK) coding scheme for covert communication over complex Gaussian channels. In particular, a transmitter Alice employs phase deflection to covertly transmit information to a receiver Bob, simultaneously ensuring that the communication intent is concealed from a warden Willie. The utilization of phase deflection allows Alice to improve the transmission rate by leveraging

Valuable information and knowledge can be learned from users’ location traces and support various location-based applications such as intelligent traffic control, incident response, and COVID-19 contact tracing. However, due to privacy concerns, no authority could simply collect users’ private location traces for mining or even publishing. To echo such concerns, local differential privacy (LDP) enables

Continuous authentication based on behavioral biometrics is effective and crucial as user behaviors are not easily copied. However, relying solely on one behavioral biometric limits the accuracy of continuous authentication. Therefore, a continuous authentication system based on multimodal behavioral biometrics fusion is proposed in this study, which fuses three modalities: contextual behavior, mouse

Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded

Recent advancements in adversarial attack research have seen a transition from white-box to black-box and even no-box threat models, greatly enhancing the practicality of these attacks. However, existing no-box attacks focus on instance-specific perturbations, leaving more powerful universal adversarial perturbations (UAPs) unexplored. This study addresses a crucial question: can UAPs be generated

Wide Area Measurement Systems (WAMSs) are used in power networks to improve the situational awareness of the operator, as well as to facilitate real-time control and protection decisions. In WAMSs, Phasor Data Concentrators (PDCs) collect time-synchronized data of Phasor Measurement Units (PMUs) through the communication system, and direct it to the control center to be used in wide-area control and

Utility-optimized local differential privacy (ULDP) considers input domain including non-sensitive values which reduces utility loss by leaking some non-sensitive values, without lowering protection to any sensitive one compared with local differential privacy (LDP). The existing ULDP mechanisms are designed under $\epsilon $ -ULDP which preserve sensitive values under $\epsilon $ -LDP. Nevertheless

Federated learning (FL) allows multiple parties, each holding a dataset, to jointly train a model without leaking any information about their own datasets. In this paper, we focus on vertical FL (VFL). In VFL, each party holds a dataset with the same sample space and different feature spaces. All parties should first agree on the training dataset in the ID alignment phase. However, existing works may

Byzantine-robust federated learning (FL) endeavors to empower service providers in acquiring a precise global model, even in the presence of potentially malicious FL clients. While considerable strides have been taken in the development of robust aggregation algorithms for FL in recent years, their efficacy is confined to addressing particular forms of Byzantine attacks, and they exhibit vulnerabilities

The vehicular ad hoc network (VANET) is a basic component of intelligent transportation systems. Due to the growing security and privacy-preserving requirements of the VANET, a lot of conditional privacy-preserving authentication (CPPA) protocols have been proposed in recent years. Unfortunately, the traditional CPPA protocols, which are based on a trusted authority (TA) and rely solely on classical

The Asmuth and Bloom threshold secret sharing (AB-SS) is a classical introduction of the Chinese remainder theorem (CRT) to secret sharing, offering low computational complexity compared to other branches of secret sharing. For decades, numerous schemes have been proposed for practical applications of AB-SS, such as secret image sharing (SIS). However, in terms of security, AB-SS has proved to be neither