Resilient Privacy Protection for Location-Based Services through Decentralization

Author:

Jin Hongyu1ORCID,Papadimitratos Panos2ORCID

Affiliation:

1. Networked Systems Security Group, KTH Royal Institute of Technology, Stockholm, Sweden

2. Networked Systems Security Group, KTH Royal Institute of Technology, and RISE SICS, Stockholm, Sweden

Abstract

Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious , collecting queries and tracking users’ whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome disadvantages of centralized schemes, eliminating anonymizers, and enhancing users’ control over sensitive information. However, an insecure decentralized system could create serious risks beyond private information leakage. More so, attacking an improperly designed decentralized LBS privacy protection scheme could be an effective and low-cost step to breach user privacy. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. We show our scheme can be deployed with off-the-shelf devices based on an experimental evaluation of an implementation in a static automotive testbed.

Funder

Swedish Foundation for Strategic Research

Knut och Alice Wallenbergs Stiftelse

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,General Computer Science

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3. Inter-regional Lens on the Privacy Preferences of Drivers for ITS and Future VANETs;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

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