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Location Privacy with Randomness Consistency

Location Privacy with Randomness Consistency Abstract Location-Based Social Network (LBSN) applications that support geo-location-based posting and queries to provide location-relevant information to mobile users are increasingly popular, but pose a location-privacy risk to posts. We investigated existing LBSNs and location privacy mechanisms, and found a powerful potential attack that can accurately locate users with relatively few queries, even when location data is well secured and location noise is applied. Our technique defeats previously proposed solutions including fake-location detection and query rate limits. To protect systems from this attack, we propose a simple, scalable, yet effective defense that quantizes the map into squares using hierarchical subdivision, consistently returns the same random result to multiple queries from the same square for posts from the same user, and responds to queries with different distance thresholds in a correlated manner, limiting the information gained by attackers, and ensuring that an attacker can never accurately know the quantized square containing a user. Finally, we verify the performance of our defense and analyze the trade-offs through comprehensive simulation in realistic settings. Surprisingly, our results show that in many environments, privacy level and user accuracy can be tuned using two independent parameters; in the remaining environments, a single parameter adjusts the tradeoff between privacy level and user accuracy. We also thoroughly explore the parameter space to provide guidance for actual deployments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

Location Privacy with Randomness Consistency

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2299-0984
eISSN
2299-0984
DOI
10.1515/popets-2016-0029
Publisher site
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Abstract

Abstract Location-Based Social Network (LBSN) applications that support geo-location-based posting and queries to provide location-relevant information to mobile users are increasingly popular, but pose a location-privacy risk to posts. We investigated existing LBSNs and location privacy mechanisms, and found a powerful potential attack that can accurately locate users with relatively few queries, even when location data is well secured and location noise is applied. Our technique defeats previously proposed solutions including fake-location detection and query rate limits. To protect systems from this attack, we propose a simple, scalable, yet effective defense that quantizes the map into squares using hierarchical subdivision, consistently returns the same random result to multiple queries from the same square for posts from the same user, and responds to queries with different distance thresholds in a correlated manner, limiting the information gained by attackers, and ensuring that an attacker can never accurately know the quantized square containing a user. Finally, we verify the performance of our defense and analyze the trade-offs through comprehensive simulation in realistic settings. Surprisingly, our results show that in many environments, privacy level and user accuracy can be tuned using two independent parameters; in the remaining environments, a single parameter adjusts the tradeoff between privacy level and user accuracy. We also thoroughly explore the parameter space to provide guidance for actual deployments.

Journal

Proceedings on Privacy Enhancing Technologies – de Gruyter

Published: Oct 1, 2016

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    Ardagna
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    Papadopoulos
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  • pierre Three protocols for location privacy In pages
    Zhong
  • Combine : leveraging the power of wireless peers through collaborative downloading In Proceedings of the th international conference on Mobile systems applications and services pages
    Ananthanarayanan
  • Where s wally Precise user discovery attacks in location proximity services In Proceedings of the nd SIGSAC Conference on Computer and Communications pages
    Polakis
  • Yelp http www yelp com
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    Schilit
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    Harremoës
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    Gedik
  • On the anonymity of home work location pairs In pages
    Golle
  • Location privacy via private proximity testing In
    Narayanan