A Privacy-Preserving Fuzzy Localization Scheme with CSI Fingerprint

CSI fingerprint localization is an advanced and promising technique for indoor localization, which identifies the user's location by mapping his measured CSI against the server's CSI fingerprint database. This approach is highlighted due to its high granularity for location distinction and strong robustness to noise disturbances, but it also causes potential privacy leakage for the three participants in localization process: the user, the server, and the AP. Currently, there has been little research done on this issue, and the existing work often ignores the privacy concern on the AP. To fill the gap, this paper develops a privacypreserving fuzzy localization scheme with CSI fingerprint. On one hand, it leverages the property of CSI training to guarantee the randomness and independence of the user's measurement in each time of localization, and uses homomorphic encryption to achieve the data transmission and measurement comparison in cipher. These operations enable our scheme to preserve the location privacy of the user and APs as well as the data privacy of the server. On the other hand, the adoption of CSI fingerprint and fuzzy logic enhances the localization accuracy greatly. Through simulation experiments performed on CRAWDAD database, the efficiency of our proposed scheme is validated.

[1]  Sneha Kumar Kasera,et al.  Robust location distinction using temporal link signatures , 2007, MobiCom '07.

[2]  Fan Yang,et al.  EV-sounding: A visual assisted electronic channel sounding system , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[3]  Limin Sun,et al.  Achieving privacy preservation in WiFi fingerprint-based localization , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[4]  Juha-Pekka Makela,et al.  Indoor geolocation science and technology , 2002, IEEE Commun. Mag..

[5]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[6]  Jie Yang,et al.  Multi-lateral privacy-preserving localization in pervasive environments , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[7]  Yao Liu,et al.  Secure Distance Indicator Leveraging Wireless Link Signatures , 2014, 2014 IEEE Military Communications Conference.

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

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

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

[11]  Guobin Shen,et al.  Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service , 2014, MobiCom.

[12]  Tong Wu,et al.  Analysis of K-Means algorithm on fingerprint based indoor localization system , 2013, 2013 5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[13]  Sneha Kumar Kasera,et al.  Advancing wireless link signatures for location distinction , 2008, MobiCom '08.

[14]  Kyu-Han Kim,et al.  SAIL: single access point-based indoor localization , 2014, MobiSys.

[15]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

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

[17]  Yao Liu,et al.  Where are you from?: confusing location distinction using virtual multipath camouflage , 2014, MobiCom.

[18]  James Aweya,et al.  Performance evaluation of CIR based location fingerprinting , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).