Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics

Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous- and-malicious data user who...

Eunomia: Anonymous and Secure Vehicular Digital Forensics based on Blockchain

Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak...

Verifiable Credentials with Privacy-Preserving Tamper-Evident Revocation Mechanism

Verifiable Credential (VC) is a new standard proposed by the W3C association to facilitate the expression and verification of third-party-verified credentials on the Internet, such as passports or diplomas. However, the current VC data model lacks an explicit revocation design that guarantees the secure operations of the system, which limits its...

User-Defined Privacy-Preserving Traffic Monitoring Against n-by-1 Jamming Attack

Traffic monitoring services collect traffic reports and respond to users' traffic queries. However, the reports and queries may reveal the user's identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous...

Mnemosyne: Privacy-Preserving Ride Matching With Collusion-Resistant Driver Exclusion

Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from repetitive...

Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization

Both communication overhead and privacy are main concerns in designing distributed computing algorithms. It is very challenging to address them simultaneously as encryption methods required for privacy-preservation often incur high communication costs. In this paper, we argue that there is a fundamental link between communication efficiency...

Nereus: Anonymous and Secure Ride-Hailing Service based on Private Smart Contracts

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack,...

Privacy for 5G-Supported Vehicular Networks

Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher...

Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation

Over the past decades, privacy-preservation has received considerable attention, not only as a consequence of regulations such as the General Data Protection Regulation in the EU, but also from the fact that people are more concerned about data abuse as the world is becoming increasingly digitized. In this paper we propose a convex optimization...

Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks

In many applications of wireless sensor networks, it is important that the privacy of the nodes of the network be protected. Therefore, privacy-preserving algorithms have received quite some attention recently. In this paper, we propose a novel convex optimization-based solution to the problem of privacy-preserving distributed average consensus....