Achieving privacy preservation in WiFi fingerprint-based localization

WiFi fingerprint-based localization is regarded as one of the most promising techniques for indoor localization. The location of a to-be-localized client is estimated by mapping the measured fingerprint (WiFi signal strengths) against a database owned by the localization service provider. A common concern of this approach that has never been addressed in literature is that it may leak the client's location information or disclose the service provider's data privacy. In this paper, we first analyze the privacy issues of WiFi fingerprint-based localization and then propose a Privacy-Preserving WiFi Fingerprint Localization scheme (PriWFL) that can protect both the client's location privacy and the service provider's data privacy. To reduce the computational overhead at the client side, we also present a performance enhancement algorithm by exploiting the indoor mobility prediction. Theoretical performance analysis and experimental study are carried out to validate the effectiveness of PriWFL. Our implementation of PriWFL in a typical Android smartphone and experimental results demonstrate the practicality and efficiency of PriWFL in real-world environments.

[1]  Stefan Katzenbeisser,et al.  Privacy-Preserving Face Recognition , 2009, Privacy Enhancing Technologies.

[2]  Guanhua Yan,et al.  Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking , 2013, IEEE Journal on Selected Areas in Communications.

[3]  Shucheng Yu,et al.  Efficient privacy-preserving biometric identification in cloud computing , 2013, 2013 Proceedings IEEE INFOCOM.

[4]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[5]  Wei Cheng,et al.  Routing for Information Leakage Reduction in Multi-channel Multi-hop Ad-Hoc Social Networks , 2012, WASA.

[6]  Andreas Haeberlen,et al.  Practical robust localization over large-scale 802.11 wireless networks , 2004, MobiCom '04.

[7]  Frank Stajano,et al.  Mix zones: user privacy in location-aware services , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[8]  Ivan Damgård,et al.  A Generalisation, a Simplification and Some Applications of Paillier's Probabilistic Public-Key System , 2001, Public Key Cryptography.

[9]  Xiuzhen Cheng,et al.  A Hybrid Rogue Access Point Protection Framework for Commodity Wi-Fi Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[10]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[11]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[12]  Mario Gerla,et al.  FreeLoc: Calibration-free crowdsourced indoor localization , 2013, 2013 Proceedings IEEE INFOCOM.

[13]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[14]  Jonathan Katz,et al.  Efficient Privacy-Preserving Biometric Identification , 2011, NDSS.

[15]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[16]  Xi Fang,et al.  Truthful incentive mechanisms for k-anonymity location privacy , 2013, 2013 Proceedings IEEE INFOCOM.

[17]  Yuguang Fang,et al.  A game-theoretic approach for achieving k-anonymity in Location Based Services , 2013, 2013 Proceedings IEEE INFOCOM.