Online ride-sharing services (RSS) have become very popular owing to increased awareness of environmental concerns and as a response to increased traffic congestion. To request a ride, users submit their locations and route information for ride matching to a service provider (SP), leading to possible privacy concerns caused by leakage of users' location data. We propose QuickPool, an efficient SP-aided RSS solution that can obliviously match multiple riders and drivers simultaneously,...

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES (Decentralized Encrypted Storage), which incorporates three essential elements:...

Syndrome-based early epidemic warning plays a vital role in preventing and controlling unknown epidemic outbreaks. It monitors the frequency of each syndrome, issues a warning if some frequency is aberrant, identifies potential epidemic outbreaks, and alerts governments as early as possible. Existing systems adopt a cloud-assisted paradigm to achieve cross-facility statistics on the syndrome frequencies. However, in these systems, all symptom data would be directly leaked to the cloud, which...

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication. Tackling a widely used distance...

Machine learning is a widely-used tool for analysing large datasets, but increasing public demand for privacy preservation and the corresponding introduction of privacy regulations have severely limited what data can be analysed, even when this analysis is for societal benefit. Homomorphic encryption, which allows computation on encrypted data, is a natural solution to this dilemma, allowing data to be analysed without sacrificing privacy. Because homomorphic encryption is computationally...

Decentralized finance based on blockchain has experienced rapid development. To safeguard the privacy of participants, decentralized anonymous payment (DAP) systems such as ZCash and Zether have emerged. These systems employ cryptographic techniques to conceal the trader addresses and payment amounts. However, this anonymity presents challenges in terms of regulation. To address this issue, we propose the Walsh-DAP (WDAP) scheme, an efficient and generic regulation scheme for...

Homomorphic Encryption~(HE) is used in many fields including information storage, data protection, privacy preservation, blockchain, and authentication. HE allows an untrusted third party to perform algebraic operations on encrypted data. Protecting the results of HE against accidental or malicious tampering attacks is still an open research challenge. In this paper, we introduce a lightweight technique that allows a data owner to verify the integrity of HE results performed in the cloud....

Integrating traditional Internet (web2) identities with blockchain (web3) identities presents considerable obstacles. Conventional solutions typically employ a mapping strategy, linking web2 identities directly to specific blockchain addresses. However, this method can lead to complications such as fragmentation of identifiers across disparate networks. To address these challenges, we propose a novel scheme, Address Abstraction (AA), that circumvents the need for direct mapping. AA scheme...

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT. In...

Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users' privacy. However, existing functional encryption implementations are still very time-consuming for practical...

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design....

Information-theoretical privacy relies on randomness. Representatively, Differential Privacy (DP) has emerged as the gold standard to quantify the individual privacy preservation provided by given randomness. However, almost all randomness in existing differentially private optimization and learning algorithms is restricted to noise perturbation. In this paper, we set out to provide a privacy analysis framework to understand the privacy guarantee produced by other randomness commonly used in...

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard...

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy-preservation...

With the rapid development of information technology and the continuous evolution of personalized services, huge amounts of data are accumulated by the large Internet company in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional privacy persistence in different information systems. However, the following problems such as the short board effect of privacy information preservation among different information systems and the difficulty of...

Homomorphic encryption has the purpose to allow computations on encrypted data, without the need for decryption other than that of the final result. This could provide an elegant solution to the problem of privacy preservation in data-based applications, such as those provided and/or facilitated by machine learning techniques, but several limitations and open issues hamper the fulfillment of this plan. In this work we assess the possibility for homomorphic encryption to fully implement its...

Decentralized blockchains offer attractive advantages over traditional payments such as the ability to operate without a trusted authority and increased user privacy. However, the verification of blockchain payments requires the user to download and process the entire chain which can be infeasible for resource-constrained devices, such as mobile phones. To address such concerns, most major blockchain systems support lightweight clients that outsource most of the computational and storage...

This paper presents a unified framework that supports different types of privacy-preserving search queries over encrypted cloud data. In the framework, users can perform any of the multi-keyword search, range search and k-nearest neighbor search operations in a privacy-preserving manner. All three types of queries are transformed into predicate-based search leveraging bucketization, locality sensitive hashing and homomorphic encryption techniques. The proposed framework is implemented using...

Oblivious RAM (ORAM) is a security-provable approach for protecting clients' access patterns to remote cloud storage. Recently, numerous ORAM constructions have been proposed to improve the communication efficiency of the ORAM model, but little attention has been paid to the storage efficiency. The state-of-the-art ORAM constructions have the storage overhead of $O(N)$ or $O(N\log N)$ blocks at the server, when $N$ data blocks are hosted. To fill the blank, this paper proposes...

The problem raised by incremental encryption is the overhead due to the larger storage space required by the provision of random blocks together with the ciphered versions of a given document. Besides, permitting variable-length modifications on the ciphertext leads to privacy preservation issues. In this paper we present incremental encryption schemes which are space-efficient, byte-wise incremental and which preserve perfect privacy in the sense that they hide the fact that an update...

In this paper, we revisit the private over-threshold data aggregation problem, and formally define the problem's security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise a novel cryptographic construction that comes in two schemes; a fully decentralized construction and its practical but semi-decentralized variant. Both schemes are provably secure in the semi-honest model. We analyze the...

In this paper, we revisit the private top-κ data aggregation problem. First we formally define the problem’s security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise a novel cryptographic construction that comes in two schemes; a fully decentralized simple construction and its practical and semi-decentralized variant. Both schemes are provably secure in the semi-honest model. We analyze the...

With the wide use of online social networks (OSNs), the problem of data privacy has attracted much attention. Several approaches have been proposed to address this issue. One of privacy management approaches for OSN leverages a key management technique to enable a user to simply post encrypted contents so that only users who can satisfy the associate security policy can derive the key to access the data. However, the key management policies of existing schemes may grant access to...

Gathering and processing sensitive data is a difficult task. In fact, there is no common recipe for building the necessary information systems. In this paper, we present a provably secure and efficient general-purpose computation system to address this problem. Our solution - SHAREMIND - is a virtual machine for privacy-preserving data processing that relies on share computing techniques. This is a standard way for securely evaluating functions in a multi-party computation environment. The...