Semantic-aware dummy selection for location privacy preservation

Shu Chen, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Citations (Scopus)

Abstract

With the development of smart devices and mobile positioning technologies, location-based services (LBS) has become more and more popular. While enjoying the convenience and entertainments provided by LBS, users are vulnerable to the increased privacy leakages of locations as another kind of quasiidentifiers. Most existing location privacy preservation algorithms are based on region cloaking which blurs the exact position into a region, and hence prone to inaccuracies of query results. Dummy-based approaches for location privacy preservation proposed recently overcome the above problem, but did not consider the problem of location semantic homogeneity. In this paper, we propose the Dummy Selection on Maximizing Minimum Distance (MaxMinDistDS) and simplified MaxMinDistDS (SimpMaxMinDistDS) that take into account both semantic diversity and physical dispersion of locations. MaxMinDistDS solves this dual-objective optimization problem by a greedy approach of maximizing first semantic diversity and then physical dispersion, and SimpMaxMinDistDS solves a simplified problem of single-objective optimization by uniting the two objectives together in order to improve the efficiency. Besides, we introduce a simplified way of computing location semantic distances by establishing a location semantic tree (LST) based on the hierarchy of locations and transforming the semantic distance into hops between nodes in LST. The efficiency and effectiveness of the proposed algorithms have been validated by a set of carefully designed experiments. The experimental results also show that our algorithms significantly improve the privacy level, compared to other dummy-based solutions.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages752-759
Number of pages8
ISBN (Electronic)9781509032051
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventJoint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016 - Tianjin, China
Duration: 23 Aug 201626 Aug 2016

Publication series

NameProceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

Conference

ConferenceJoint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Country/TerritoryChina
CityTianjin
Period23/08/1626/08/16

Keywords

  • Dummy selection
  • Location semantic tree
  • MaxMin distance
  • Physical dispersion
  • Semantic diversity

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