MAPP: An efficient multi-location task allocation framework with personalized location privacy-protecting in spatial crowdsourcing

Article Automation & Control Systems

Enhancement of opacity for distributed state estimation in cyber-physical systems

Liwei An et al.

Summary: This paper presents a new framework for opacity in distributed state estimation, introduces two opacity-enhancing algorithms, and establishes necessary and sufficient conditions to ensure the secrecy of the system's state.

AUTOMATICA (2022)

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Article Automation & Control Systems

Privacy-preserving dynamic average consensus via state decomposition: Case study on multi-robot formation control

Kaixiang Zhang et al.

Summary: This paper presents a state decomposition-based privacy preservation scheme to protect the privacy of agents in a decentralized control/estimation framework. The proposed scheme preserves the convergence of average consensus while protecting the agents' privacy. It is applied to the formation control of multiple mobile robots, and its effectiveness is demonstrated through numerical simulation.

AUTOMATICA (2022)

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Article Computer Science, Information Systems

Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability

Chenxi Qiu et al.

Summary: In this paper, the authors address the issue of Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. They propose a location obfuscation strategy to minimize quality loss while satisfying geo-indistinguishability. They approximate VLP to a linear programming problem and employ discretization and constraint reduction techniques to improve time efficiency. Experimental results demonstrate the superiority of their approach in terms of both quality-of-service and privacy.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Computer Science, Hardware & Architecture

Proxy-Free Privacy-Preserving Task Matching with Efficient Revocation in Crowdsourcing

Jiangang Shu et al.

Summary: This paper explores the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers, and proposes a proxy-free task matching scheme. Efficient worker revocation mechanisms are designed to revoke workers with minimal overhead, leading to a scalable and non-interactive task-worker matching over encrypted data. The proposed scheme is proven to be secure and outperforms existing state-of-the-art work.

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING (2021)

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Article Computer Science, Hardware & Architecture

Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation

Leye Wang et al.

Summary: This research proposes a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Evaluation results verify the effectiveness of the framework, achieving up to 47 percent shorter average travel distance.

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING (2021)

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Article Computer Science, Information Systems

Data privacy preservation algorithm with k-anonymity

Waranya Mahanan et al.

Summary: This study introduces a heuristic privacy preservation algorithm on Identical Generalization Hierarchy (IGH) data, developed from observations on the anonymous property of the problem structure for more efficient privacy protection. Experimental results demonstrate that the proposed algorithm effectively preserves data privacy and reduces the number of visited nodes by up to 21% compared to the most efficient existing algorithm.

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2021)

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Article Computer Science, Information Systems