Achieving Efficient and Privacy-Preserving (,)-Core Query over Bipartite Graphs in Cloud

Guan, Yunguo; Lu, Rongxing; Zheng, Yandong; Zhang, Songnian; Shao, Jun; Wei, Guiyi

doi:10.1109/tdsc.2022.3169386

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Nevertheless, they may possess an interest in the information pertaining to other entities and have the capability to compromise data privacy through the utilization of their knowledge. Additionally, it is assumed that the two cloud servers are non-colluding, a common and reasonable assumption in numerous application scenarios [34][35][36][37]. Furthermore, it is further assumed that neither the owner nor the suspect will engage in collusion with the cloud servers.…”

Section: Data Privacymentioning

confidence: 99%

A Privacy-Preserving Testing Framework for Copyright Protection of Deep Learning Models

Wei,

Wang,

Wang

et al. 2023

Electronics

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Deep learning is widely utilized to acquire predictive models for mobile crowdsensing systems (MCSs). These models significantly improve the availability and performance of MCSs in real-world scenarios. However, training these models requires substantial data resources, rendering them valuable to their owners. Numerous protection schemes have been proposed to mitigate potential economic loss arising from legal issues pertaining to model copyright. Although capable of providing copyright verification, these schemes either compromise the model utility or prove ineffective against adversarial attacks. Additionally, the privacy concern surrounding copyright verification is noteworthy, given the increasing privacy concerns among model owners. This paper introduces a privacy-preserving testing framework for copyright protection (PTFCP) comprising multiple protocols. Our protocols adhere to the two-cloud server model, where the owner and the suspect transmit their model output to non-colluding servers for evaluating model similarity through the public-key cryptosystem with distributed decryption (PCDD) and garbled circuits. Additionally, we have developed novel techniques to enable secure differentiation for absolute values. Our experiments in real-world datasets demonstrate that our protocols in the PTFCP successfully operate under numerous copyright violation scenarios, such as finetuning, pruning, and extraction.

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Section: Data Privacymentioning

confidence: 99%

A Privacy-Preserving Testing Framework for Copyright Protection of Deep Learning Models

Wei,

Wang,

Wang

et al. 2023

Electronics

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Privacy-Preserving Approximate Minimum Community Search on Large Networks

Sun,

Yu,

2024

IEEE Trans.Inform.Forensic Secur.

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A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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Achieving Efficient and Privacy-Preserving (,)-Core Query over Bipartite Graphs in Cloud

Cited by 3 publications

References 29 publications

A Privacy-Preserving Testing Framework for Copyright Protection of Deep Learning Models

A Privacy-Preserving Testing Framework for Copyright Protection of Deep Learning Models

Privacy-Preserving Approximate Minimum Community Search on Large Networks

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

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