Protecting query privacy with differentially private k-anonymity in location-based services

Protecting query privacy with differentially private k-anonymity in location-based services Nowadays, location-based services (LBS) are facilitating people in daily life through answering LBS queries. However, privacy issues including location privacy and query privacy arise at the same time. Existing works for protecting query privacy either work on trusted servers or fail to provide sufficient privacy guarantee. This paper combines the concepts of differential privacy and k-anonymity to propose the notion of differentially private k-anonymity (DPkA) for query privacy in LBS. We recognize the sufficient and necessary condition for the availability of 0-DPkA and present how to achieve it. For cases where 0-DPkA is not achievable, we propose an algorithm to achieve 𝜖-DPkA with minimized 𝜖. Extensive simulations are conducted to validate the proposed mechanisms based on real-life datasets and synthetic data distributions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Personal and Ubiquitous Computing Springer Journals

Protecting query privacy with differentially private k-anonymity in location-based services

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; User Interfaces and Human Computer Interaction; Computer Science, general; Personal Computing; Mobile Computing
ISSN
1617-4909
eISSN
1617-4917
D.O.I.
10.1007/s00779-018-1124-7
Publisher site
See Article on Publisher Site

Abstract

Nowadays, location-based services (LBS) are facilitating people in daily life through answering LBS queries. However, privacy issues including location privacy and query privacy arise at the same time. Existing works for protecting query privacy either work on trusted servers or fail to provide sufficient privacy guarantee. This paper combines the concepts of differential privacy and k-anonymity to propose the notion of differentially private k-anonymity (DPkA) for query privacy in LBS. We recognize the sufficient and necessary condition for the availability of 0-DPkA and present how to achieve it. For cases where 0-DPkA is not achievable, we propose an algorithm to achieve 𝜖-DPkA with minimized 𝜖. Extensive simulations are conducted to validate the proposed mechanisms based on real-life datasets and synthetic data distributions.

Journal

Personal and Ubiquitous ComputingSpringer Journals

Published: Mar 9, 2018

References

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