Privacy-preserving Wi-Fi Analytics

Open access

Abstract

As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.

[1] M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep learning with differential privacy. In E. R. Weippl, S. Katzenbeisser, C. Kruegel, A. C. Myers, and S. Halevi, editors, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pages 308–318, Vienna, Austria, October 2016. ACM.

[2] U. G. Acer, G. Vanderhulst, A. Masshadi, A. Boran, C. Forlivesi, P. M. Scholl, and F. Kawsar. Capturing Personal and Crowd Behavior with Wi-Fi Analytics. In Proceedings of the 3rd International on Workshop on Physical Analytics, WPA ’16, pages 43–48, New York, NY, USA, 2016. ACM.

[3] M. Alaggan, S. Gambs, and A.-M. Kermarrec. BLIP: Non-Interactive Differentially-Private Similarity Computation on Bloom Filters. In Proceedings of the 14th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS’12), Toronto, Canada, October, 2012.

[4] M. Alaggan, S. Gambs, S. Matwin, and M. Tuhin. Sanitization of Call Detail Records via Differentially-Private Bloom Filters. In P. Samarati, editor, Data and Applications Security and Privacy XXIX - 29th Annual IFIP WG 11.3 Working Conference, DBSec 2015, Fairfax, VA, USA, July 13-15, 2015, Proceedings, volume 9149 of Lecture Notes in Computer Science, pages 223–230. Springer, 2015.

[5] M. Alaggan, M. Cunche, and M. Minier. Non-interactive (t, n)-Incidence Counting from Differentially Private Indicator Vectors. In Proceedings of the 2017 ACM on International Workshop on Security And Privacy Analytics, IWSPA@CODASPY 2017, Scottsdale, AZ, USA, March 2017. ACM.

[6] M. S. Alvim, M. E. Andrés, K. Chatzikokolakis, and C. Palamidessi. On the relation between differential privacy and quantitative information flow. In L. Aceto, M. Henzinger, and J. Sgall, editors, Automata, Languages and Programming - 38th International Colloquium, ICALP 2011, Zurich, Switzerland, July 4-8, 2011, Proceedings, Part II, volume 6756 of Lecture Notes in Computer Science, pages 60–76. Springer, 2011.

[7] M. Arlitt and T. Jin. 1998 World Cup Web Site Access Logs, August 1998. URL http://www.acm.org/sigcomm/ITA/.

[8] R. Balu, T. Furon, and S. Gambs. Challenging Differential Privacy: The Case of Non-Interactive Mechanisms. In ESORICS, pages 146–164, 2014.

[9] S. Bera and K. Rao. Estimation of origin-destination matrix from traffic counts: the state of the art. European Transport/Trasporti Europei, 49:3–23, 2011.

[10] G. Bianchi, L. Bracciale, and P. Loreti. “Better Than Nothing” Privacy with Bloom Filters: To What Extent? In International Conference on Privacy in Statistical Databases, pages 348–363. Springer, 2012.

[11] B. H. Bloom. Space/Time Trade-offs in Hash Coding with Allowable Errors. Commun. ACM, 13(7):422–426, July 1970. ISSN 0001-0782.

[12] P. Bose, H. Guo, E. Kranakis, A. Maheshwari, P. Morin, J. Morrison, M. H. M. Smid, and Y. Tang. On the falsepositive rate of bloom filters. Inf. Process. Lett., 108(4): 210–213, 2008.

[13] C. Bouchenard. JC Decaux’s pedestrian tracking system blocked by french data regulator. Marketinglaw, 2015. URL http://marketinglaw.osborneclarke.com/advertising-regulation/jc-decauxs-pedestrian-tracking-system-blocked-by-french-data-regulator/.

[14] A. Z. Broder and M. Mitzenmacher. Survey: Network Applications of Bloom Filters: A Survey. Internet Mathematics, 1(4): 485–509, 2003.

[15] J. W. S. Brown, O. Ohrimenko, and R. Tamassia. Haze: Privacy-preserving real-time traffic statistics. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’ 13, pages 540–543, New York, NY, USA, 2013. ACM.

[16] J. Cao, B. Carminati, E. Ferrari, and K. L. Tan. CASTLE: A delay-constrained scheme for ks-anonymizing data streams. In 2008 IEEE 24th International Conference on Data Engineering, pages 1376–1378, Apr. 2008.

[17] K. Chung, M. Mitzenmacher, and S. P. Vadhan. Why simple hash functions work: Exploiting the entropy in a data stream. Theory of Computing, 9:897–945, 2013.

[18] S. Clifford and Q. Hardy. Attention, Shoppers: Store Is Tracking Your Cell. The New York Times, 2013. URL http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.html?pagewanted=all.

[19] A. De. Lower bounds in differential privacy. In R. Cramer, editor, Theory of Cryptography - 9th Theory of Cryptography Conference, TCC 2012, Taormina, Sicily, Italy, March 19-21, 2012. Proceedings, volume 7194 of Lecture Notes in Computer Science, pages 321–338. Springer, 2012.

[20] L. Demir, M. Cunche, and C. Lauradoux. Analysing the privacy policies of Wi-Fi trackers. pages 39–44. ACM Press, 2014.

[21] I. Dinur and K. Nissim. Revealing information while preserving privacy. In F. Neven, C. Beeri, and T. Milo, editors, Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 9-12, 2003, San Diego, CA, USA, pages 202–210. ACM, 2003.

[22] C. Dwork. Differential Privacy. In M. Bugliesi, B. Preneel, V. Sassone, and I. Wegener, editors, Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (ICALP’06), Part II, volume 4052 of Lecture Notes in Computer Science, pages 1–12, Venice, Italy, 2006. Springer.

[23] C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In L. J. Schulman, editor, Proceedings of the 42nd ACM Symposium on Theory of Computing, STOC 2010, Cambridge, Massachusetts, USA, 5-8 June 2010, pages 715–724. ACM, 2010.

[24] C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing, pages 715–724. ACM, 2010.

[25] C. Dwork, M. Naor, T. Pitassi, G. N. Rothblum, and S. Yekhanin. Pan-Private Streaming Algorithms. In A. C. Yao, editor, Proceedings of the 1st Symposium on Innovations in Computer Science (ICS’10), pages 66–80, Tsinghua University, Beijing, China, 2010. Tsinghua University Press.

[26] U. Erlingsson, V. Pihur, and A. Korolova. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. pages 1054–1067. ACM Press, 2014.

[27] K. Fawaz, K.-H. Kim, and K. G. Shin. Privacy vs. Reward in Indoor Location-Based Services. Proceedings on Privacy Enhancing Technologies, 2016(4):102–122, 2016. ISSN 2299-0984. 00000.

[28] Federal Trade Commisioin. Retail tracking firm settles ftc charges it misled consumers about opt out choices, 2015. URL https://www.ftc.gov/news-events/press-releases/2015/04/retail-tracking-firm-settles-ftc-charges-it-misled-consumers.

[29] Future of Privacy Forum. Mobile location analytics code of conduct, 2013. URL https://fpf.org/wp-content/uploads/10.22.13-FINAL-MLA-Code.pdf.

[30] N. Gonçalves, R. José, and C. Baquero. Privacy Preserving Gate Counting with Collaborative Bluetooth Scanners. In R. Meersman, T. Dillon, and P. Herrero, editors, On the Move to Meaningful Internet Systems: OTM 2011 Workshops, number 7046 in Lecture Notes in Computer Science, pages 534–543. Springer Berlin Heidelberg, Oct. 2011.

[31] P. Higgins and L. Tien. Mobile tracking code of conduct falls short of protecting consumers. Electronic Frontier Foundation, 2013. URL https://www.eff.org/fr/deeplinks/2013/10/mobile-tracking-code-conduct-falls-short-protecting-consumers.

[32] C. Kopp, M. Mock, and M. May. Privacy-preserving distributed monitoring of visit quantities. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’12, pages 438–441, New York, NY, USA, 2012. ACM.

[33] P. A. Laharotte, R. Billot, E. Come, L. Oukhellou, A. Nantes, and N. E. E. Faouzi. Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network. IEEE Transactions on Intelligent Transportation Systems, 16 (3):1439–1448, June 2015. ISSN 1524-9050.

[34] J. Li, B. C. Ooi, and W. Wang. Anonymizing streaming data for privacy protection. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, pages 1367–1369. IEEE, 2008.

[35] R. Lim, M. Zimmerling, and L. Thiele. Passive, Privacy-Preserving Real-Time Counting of Unmodified Smartphones via ZigBee Interference. In 2015 International Conference on Distributed Computing in Sensor Systems, pages 115–126, June 2015.

[36] Liyue Fan and Li Xiong. Adaptively Sharing Time-Series with Differential Privacy. Technical report, Jan. 2013.

[37] J. O. Malley. Here’s what tfl learned from tracking your phone on the tube. Gizmodo UK, 2017. URL http://www.gizmodo.co.uk/2017/02/heres-what-tfl-learned-from-tracking-your-phone-on-the-tube/.

[38] J. Martin, T. Mayberry, C. Donahue, L. Foppe, L. Brown, C. Riggins, E. C. Rye, and D. Brown. A Study of MAC Address Randomization in Mobile Devices and When it Fails. Proceedings on Privacy Enhancing Technologies, 2017(4): 268–286, 2017.

[39] F. McSherry. Privacy Integrated Queries: an Extensible Platform for Privacy-Preserving Data Analysis. Commun. ACM, 53(9):89–97, 2010.

[40] L. Melis, G. Danezis, and E. D. Cristofaro. Efficient private statistics with succinct sketches. CoRR, abs/1508.06110, 2015.

[41] A. Musa and J. Eriksson. Tracking unmodified smartphones using wi-fi monitors. In Proceedings of the 10th ACM conference on embedded network sensor systems, pages 281–294. ACM, 2012.

[42] R. A. Popa, A. J. Blumberg, H. Balakrishnan, and F. H. Li. Privacy and accountability for location-based aggregate statistics. In Proceedings of the 18th ACM Conference on Computer and Communications Security, CCS ’11, pages 653–666, New York, NY, USA, 2011. ACM.

[43] A. Pyrgelis, E. De Cristofaro, and G. J. Ross. Privacy-friendly mobility analytics using aggregate location data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, page 34. ACM, 2016.

[44] A. E. C. Redondi, D. Sanvito, and M. Cesana. Passive Classification of Wi-Fi Enabled Devices. pages 51–58. ACM Press, 2016.

[45] E. Shi, H. T. H. Chan, E. Rieffel, R. Chow, and D. Song. Privacy-preserving aggregation of time-series data. In Annual Network & Distributed System Security Symposium (NDSS). Internet Society., 2011.

[46] S. J. Swamidass and P. Baldi. Mathematical correction for fingerprint similarity measures to improve chemical retrieval. Journal of Chemical Information and Modeling, 47(3):952–964, 2007.

[47] O. Waltari and J. Kangasharju. The Wireless Shark: Identifying WiFi Devices Based on Probe Fingerprints. In Proceedings of the First Workshop on Mobile Data, MobiData ’16, pages 1–6, New York, NY, USA, 2016. ACM. 00000.

[48] K. Whang, B. T. V. Zanden, and H. M. Taylor. A linear-time probabilistic counting algorithm for database applications. ACM Trans. Database Syst., 15(2):208–229, 1990.

[49] Y. Zeng, P. H. Pathak, and P. Mohapatra. Analyzing Shopper’s Behavior Through WiFi Signals. In Proceedings of the 2Nd Workshop on Workshop on Physical Analytics, WPA ’15, pages 13–18, New York, NY, USA, 2015. ACM.

[50] B. Zhou, Y. Han, J. Pei, B. Jiang, Y. Tao, and Y. Jia. Continuous Privacy Preserving Publishing of Data Streams. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’09, pages 648–659, New York, NY, USA, 2009. ACM.

[51] G. Zipf. Human behavior and the principle of least effort: an introduction to human ecology. Addison-Wesley Press, 1949.

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