Encrypting wireless network traces to protect user privacy: A case study for smart campus

Wireless network traces have been widely used to understand human behaviors and provide value-added services. Sanitization based techniques have been shown to be severely lacking in protecting sensitive user information embedded in such traces. In this paper, we take an encryption based approach that provides much stronger protection of user privacy. One challenge in encrypting wireless network traces is how to encrypt time range while maintaining the utility of the traces. We propose two practical encryption techniques to support queries that involve time range. These two techniques provide much stronger security guarantee than existing order preserving encryption schemes, and present different tradeoffs in complexity, as well as storage and network bandwidth requirement. Last, we quantify the performance of the proposed approach using a smart campus prototype. The results show that our approach only leads to moderate increase in storage, network bandwidth and computation overhead, demonstrating the practicality of our approach.

[1]  A. Helmy,et al.  Gender-based Grouping of Mobile Student Societies , 2008 .

[2]  Srinivasan Keshav,et al.  Trace-based analysis of Wi-Fi scanning strategies , 2009, MOCO.

[3]  Guanhua Yan,et al.  Privacy analysis of user association logs in a large-scale wireless LAN , 2011, 2011 Proceedings IEEE INFOCOM.

[4]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[5]  Rafail Ostrovsky,et al.  Public Key Encryption with Keyword Search , 2004, EUROCRYPT.

[6]  Ahmed Helmy,et al.  Human Behavior and Challenges of Anonymizing WLAN Traces , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[7]  Adriano J. C. Moreira,et al.  Anomaly detection in university campus WiFi zones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[8]  Rafail Ostrovsky,et al.  Searchable symmetric encryption: Improved definitions and efficient constructions , 2011, J. Comput. Secur..

[9]  Hakan Hacigümüs,et al.  Executing SQL over encrypted data in the database-service-provider model , 2002, SIGMOD '02.

[10]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[11]  Prasant Mohapatra,et al.  Improving energy efficiency of Wi-Fi sensing on smartphones , 2011, 2011 Proceedings IEEE INFOCOM.

[12]  Ahmed Helmy,et al.  Extended Abstract : Mining Behavioral Groups in Large Wireless LANs , 2007 .

[13]  Stavros Papadopoulos,et al.  Practical Private Range Search Revisited , 2016, SIGMOD Conference.

[14]  Ahmed Helmy,et al.  Modeling Spatial and Temporal Dependencies of User Mobility in Wireless Mobile Networks , 2008, IEEE/ACM Transactions on Networking.

[15]  Hari Balakrishnan,et al.  CryptDB: protecting confidentiality with encrypted query processing , 2011, SOSP.

[16]  Aggelos Kiayias,et al.  Delegatable pseudorandom functions and applications , 2013, IACR Cryptol. ePrint Arch..

[17]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[18]  Asma Ahmad Farhan,et al.  Locating emergencies in a campus using wi-fi access point association data , 2013, UbiComp.

[19]  Moni Naor,et al.  Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.

[20]  Nathan Chenette,et al.  Order-Preserving Symmetric Encryption , 2009, IACR Cryptol. ePrint Arch..

[21]  Dawn Xiaodong Song,et al.  Practical techniques for searches on encrypted data , 2000, Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000.

[22]  Rajesh Gupta,et al.  Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings , 2013, SenSys '13.

[23]  Min Zhang,et al.  Privacy-Enhancing Range Query Processing over Encrypted Cloud Databases , 2015, WISE.

[24]  Nickolai Zeldovich,et al.  An Ideal-Security Protocol for Order-Preserving Encoding , 2013, 2013 IEEE Symposium on Security and Privacy.

[25]  Yanbin Lu,et al.  Privacy-preserving Logarithmic-time Search on Encrypted Data in Cloud , 2012, NDSS.

[26]  Cengis Hasan,et al.  2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) , 2013 .

[27]  Sheng Zhong,et al.  Privacy-Preserving Queries on Encrypted Data , 2006, ESORICS.

[28]  Gene Tsudik,et al.  A Privacy-Preserving Index for Range Queries , 2004, VLDB.

[29]  Rui Li,et al.  Fast Range Query Processing with Strong Privacy Protection for Cloud Computing , 2014, Proc. VLDB Endow..

[30]  Silvio Micali,et al.  How to construct random functions , 1986, JACM.

[31]  Ramakrishnan Srikant,et al.  Order preserving encryption for numeric data , 2004, SIGMOD '04.