Michael Backes
ABSTRACT
The development of positioning technologies has resulted in an increasing amount of mobility data being available. While bringing a lot of convenience to people's life, such availability also raises serious concerns about privacy. In this paper, we concentrate on one of the most sensitive information that can be inferred from mobility data, namely social relationships. We propose a novel social relation inference attack that relies on an advanced feature learning technique to automatically summarize users' mobility features. Compared to existing approaches, our attack is able to predict any two individuals' social relation, and it does not require the adversary to have any prior knowledge on existing social relations. These advantages significantly increase the applicability of our attack and the scope of the privacy assessment. Extensive experiments conducted on a large dataset demonstrate that our inference attack is effective, and achieves between 13% to 20% improvement over the best state-of-the-art scheme. We propose three defense mechanisms -- hiding, replacement and generalization -- and evaluate their effectiveness for mitigating the social link privacy risks stemming from mobility data sharing. Our experimental results show that both hiding and replacement mechanisms outperform generalization. Moreover, hiding and replacement achieve a comparable trade-off between utility and privacy, the former preserving better utility and the latter providing better privacy.
Supplemental Material
- Berker Augır, Kévin Huguenin, Urs Hengartner, and Jean-Pierre Hubaux 2016. On the Privacy Implications of Location Semantics. Proceedings on Privacy Enhancing Technologies, Vol. 2016, 4 (2016), 165--183.Google Scholar
Cross Ref
- Michael Backes, Pascal Berrang, Anne Hecksteden, Mathias Humbert, Andreas Keller, and Tim Meyer 2016. Privacy in Epigenetics: Temporal Linkability of MicroRNA Expression Profiles Proceedings of the 25th USENIX Security Symposium (Security). USENIX, 1223--1240.Google Scholar
- Lars Backstrom, Eric Sun, and Cameron Marlow 2010. Find Me If You Can: Improving Geographical Prediction with Social and Spatial Proximity Proceedings of the 19th International Conference on World Wide Web (WWW). ACM, 61--70.Google ScholarDigital Library
- Igor Bilogrevic, Kévin Huguenin, Stefan Mihaila, Reza Shokri, and Jean-Pierre Hubaux 2015. Predicting Users' Motivations behind Location Check-ins and Utility Implications of Privacy Protection Mechanisms. In Proceedings of the 22nd Network and Distributed System Security Symposium (NDSS). Google ScholarCross Ref
- Zhiyuan Cheng, James Caverlee, and Kyumin Lee. 2010. You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 759--768.Google Scholar
- Zhiyuan Cheng, James Caverlee, Kyumin Lee, and Daniel Z. Sui 2011. Exploring Millions of Footprints in Location Sharing Services Proceedings of the 5th International Conference on Weblogs and Social Media (ICWSM). The AAAI Press, 81--88.Google Scholar
- Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and Mobility: User Movement in Location-based Social Networks Proceedings of the 17th ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 1082--1090.Google ScholarDigital Library
- David J. Crandall, Lars Backstrom, Dan Cosley, Siddharth Suri, Daniel Huttenlocher, and Jon Kleinberg 2010. Inferring Social Ties from Geographic Coincidences. Proceedings of the National Academy of Sciences, Vol. 107, 52 (2010), 22436--22441. Google ScholarCross Ref
- Ratan Dey, Zubin Jelveh, and Keith Ross 2012. Facebook Users Have Become Much More Private: A Large-Scale Study Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE, 346--352.Google Scholar
- Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nittiest V Chawla, Jinghai Rao, and Huanhuan Cao 2012. Link Prediction and Recommendation across Heterogeneous Social Networks Proceedings of the 12th International Conference on Data Mining (ICDM). IEEE, 181--190.Google Scholar
- Nathan Eagle, Alex Sandy Pentland, and David Lazer. 2009. Inferring Friendship Network Structure by Using Mobile Phone Data. Proceedings of the National Academy of Sciences, Vol. 106, 36 (2009), 15274--15278. Google ScholarCross Ref
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys). ACM, 93--100. Google ScholarDigital Library
- Aditya Grover and Jure Leskovec 2016. node2vec: Scalable Feature Learning for Networks Proceedings of the 22nd ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 855--864.Google Scholar
- Mathias Humbert, Théophile Studer, Matthias Grossglauser, and Jean-Pierre Hubaux. 2013. Nowhere to Hide: Navigating around Privacy in Online Social Networks Proceedings of the 18th European Symposium on Research in Computer Security (ESORICS). Springer, 682--699.Google Scholar
- David Jurgens. 2013. That's What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships. In Proceedings of the 7th International Conference on Weblogs and Social Media (ICWSM). The AAAI Press, 273--282.Google Scholar
- David Jurgens, Tyler Finethy, James McCorriston, Yi Tian Xu, and Derek Ruths 2015. Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice. In Proceedings of the 9th International Conference on Weblogs and Social Media (ICWSM). The AAAI Press, 188--197.Google Scholar
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a Social Network or a News Media? Proceedings of the 19th International Conference on World Wide Web (WWW). ACM, 591--600. Google ScholarDigital Library
- David Liben-Nowell and Jon Kleinberg 2007. The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, Vol. 58, 7 (2007), 1019--1031. Google ScholarDigital Library
- Ryan N Lichtenwalter, Jake T Lussier, and Nitesh V Chawla. 2010. New Perspectives and Methods in Link Prediction. Proceedings of the 16th ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 243--252.Google ScholarDigital Library
- Lydia Manikonda, Yuheng Hu, and Subbarao Kambhampati. 2014. Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram. CoRR Vol. abs/1410.8099 (2014).Google Scholar
- Jeffrey McGee, James Caverlee, and Zhiyuan Cheng. 2013. Location Prediction in Social Media Based on Tie Strength Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM). ACM, 459--468.Google Scholar
- Aditya Krishna Menon and Charles Elkan 2011. Link Prediction via Matrix Factorization. In Proceedings of the 2011 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). Springer, 437--452. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space Proceedings of the 1st International Conference on Learning Representations (ICLR).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeffrey Dean 2013. Distributed Representations of Words and Phrases and their Compositionally Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS). NIPS, 3111--3119.Google Scholar
- Prateek Mittal, Charalampos Papamanthou, and Dawn Song 2013. Preserving Link Privacy in Social Network Based Systems Proceedings of the 20th Network and Distributed System Security Symposium (NDSS).Google Scholar
- Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel 2013. Unique in the Crowd: The Privacy Bounds of Human Mobility. Scientific Reports Vol. 3 (2013), 1376. Google ScholarCross Ref
- Arvind Narayanan and Vitaly Shmatikov 2009. De-anonymizing Social Networks. In Proceedings of the 30th IEEE Symposium on Security and Privacy (S&P). IEEE, 173--187. Google ScholarDigital Library
- Alexandra-Mihaela Olteanu, Kévin Huguenin, Reza Shokri, Mathias Humbert, and Jean-Pierre Hubaux 2017. Quantifying Interdependent Privacy Risks with Location Data. IEEE Transactions on Mobile Computing Vol. 16, 3 (2017), 829--842. Google ScholarDigital Library
- Jun Pang and Yang Zhang 2015. Location Prediction: Communities Speak Louder than Friends Proceedings of the 3rd ACM on Conference on Online Social Networks (COSN). ACM, 161--171.Google Scholar
- Jun Pang and Yang Zhang 2017. DeepCity: A Feature Learning Framework for Mining Location Check-Ins Proceedings of the 11th International Conference on Web and Social Media (ICWSM). The AAAI Press, 652--655.Google Scholar
- Jun Pang and Yang Zhang 2017. Quantifying Location Sociality. In Proceedings of the 28th ACM Conference on Hypertext and Social Media (HT). ACM, 145--154. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online Learning of Social Representations Proceedings of the 20th ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 701--710.Google ScholarDigital Library
- Huy Pham, Cyrus Shahabi, and Yan Liu 2013. EBM: An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data Proceedings of the 2013 ACM Conference on Management of Data (SIGMOD). ACM, 265--276.Google Scholar
- Adam Sadilek, Henry Kautz, and Jeffrey P. Bigham. 2012. Finding Your Friends and Following Them to Where You Are Proceedings of the 5th ACM Conference on Web Search and Data Mining (WSDM). ACM, 459--468.Google Scholar
- Salvatore Scellato, Anastasios Noulas, and Cecilia Mascolo 2011. Exploiting Place Features in Link Prediction on Location-based Social Networks Proceedings of the 17th ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 1046--1054.Google Scholar
- Reza Shokri, Georgios Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux 2011. Quantifying Location Privacy. In Proceedings of the 32nd IEEE Symposium on Security and Privacy (S&P). IEEE, 247--262. Google ScholarDigital Library
- Mudhakar Srivatsa and Mike Hicks 2012. Deanonymizing Mobility Traces: Using Social Network as a Side-channel Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS). ACM, 628--637.Google Scholar
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale Information Network Embedding. Proceedings of the 24th International Conference on World Wide Web (WWW). ACM, 1067--1077. Google ScholarDigital Library
- Alastair J Walker. 1977. An Efficient Method for Generating Discrete Random Variables with General Distributions. ACM Transactions on Mathematical Software, Vol. 3, 3 (1977), 253--256. Google ScholarDigital Library
- Hongjian Wang, Zhenhui Li, and Wang-Chien Lee. 2014. PGT: Measuring Mobility Relationship using Personal, Global and Temporal Factors Proceedings of the 14th IEEE Conference on Data Mining (ICDM). IEEE, 570--579.Google Scholar
- Fei Wu and Zhenhui Li. 2016. Where Did You Go: Personalized Annotation of Mobility Records Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 589--598.Google ScholarDigital Library
- Yang Zhang and Jun Pang 2015. Distance and Friendship: A Distance-based Model for Link Prediction in Social Networks Proceedings of the 17th Asia-Pacific Web Conference (APWeb). Springer, 55--66.Google Scholar
Index Terms
- walk2friends: Inferring Social Links from Mobility Profiles
Recommendations
User-defined privacy location-sharing system in mobile online social networks
With the fusion of social networks and location-based services, location sharing is one of the most important services in mobile online social networks (mOSNs). In location-sharing services, users have to provide their location information to service ...
Location privacy for crowdsourcing applications
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous ComputingThis paper contributes to mobile crowdsourcing applications by developing a privacy preserving framework that enables users to contribute content to the community while controlling their privacy exposure. One fundamental challenge in such applications ...
B-Mobishare: Privacy-Preserving Location Sharing Mechanism in Mobile Online Social Networks
BWCCA '14: Proceedings of the 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and ApplicationsIn recent years, mobile Online Social Networks (mOSNs) have gained more and more popularity. Compared with traditional social networks, mOSNs allow user's location to come into play. Thus, location sharing becomes a fundamental component of mOSNs, and ...
Comments