Yi Ding

Assistant Professor at UT Dallas

A common conclusion is that user trace can be recovered from very little trace leakage, published mobility data has privacy vulnerabilities even with anonymization [MobiCom10, MobiCom11, SciRep13, WWW17].

Many Location-Privacy Protection Mechanisms (LPPMs) have been proposed on this topic [MobiSys14, KDD14, INFOCOM14, SIGSPATIAL15-PLACE].

Location Privacy is quantified in [IEEESSP11].

k-anonymity: is a property possessed by certain anonymized data.

Detailed Reviews

  • MobiCom10: Privacy Vulnerability of Published Anonymous Mobility Traces
    • Summary: Even anonymous published mobility trace data has privacy vulnerabilities due to spatial-temporal correlations.
  • Ubicomp10: Exploring End User Preferences for Location Obfuscation
    • Summary: Visualization and questionnaire survey on 32 GPS data, showing that privacy protection awarenesses is lacking among people
  • MobiCom11: Anonymization of Location Data Does Not Work
    • Summary: As title indicates.
  • IEEESSP11: Quantifying Location Privacy
    • Summary: Many LPPMs are proposed, but unified metric is missing to measure location privacy and performance of LPPMs. Metric is proposed in this paper.
  • SciRep13: The privacy bounds of human mobility
    • Summary: Uniqueness in human mobility is bounded, it’s easy to infer user trace.
  • MobiSys14-EnCore: Private, Context-based Communication for Mobile Social Apps
    • Taxonomy: LPPM in application
    • Summary: New LPPM for social-networking APP,
  • KDD14: Privacy Preserving Release of Spatio-temporal Density in Paris
    • Taxonomy: LPPM in data releasing
    • Summary: New LPPM is provided in data release with differential privacy.
  • INFOCOM14-DLS: Achieving k-anonymity in Privacy-Aware Location-Based Services
    • Taxonomy: LPPM
    • Summary: Introducing dummy locations.
  • SIGSPATIAL15-PLACE: Privacy-Preserving Inference of Social Relationships from Location Data
    • Taxonomy: LPPM in computing
    • Summary: An framework is envisioned to preserve the priivacy when inferring social relations.
  • WWW17: User Privacy Is NOT Preserved in Aggregated Mobility Data
    • Summary: Same scope as [MobiCom10], [MobiCom1] and [SciRep13].

Ref

[MobiCom10] Chris Y T Ma and David K Y Yau. 2010. Privacy Vulnerability of Published Anonymous Mobility Traces. In ACM MobiCom, 185–196.

[Ubicomp10] A.J. Bernheim Brush, John Krumm, and James Scott. 2010. Exploring end user preferences for location obfuscation, location-based services, and the value of location. In ACM Ubicomp, 95.

[MobiCom11] Hui Zang and Jean Bolot. 2011. Anonymization of Location Data Does Not Work: A Large-Scale Measurement Study Hui. In ACM MobiCom, 299–318.

[IEEESSP11] Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux. 2011. Quantifying Location Privacy. In IEEE Symposium on Security and Privacy

[SciRep13] Yves Alexandre De Montjoye, César A. Hidalgo, Michel Verleysen, and Vincent D. Blondel. 2013. Unique in the Crowd: The privacy bounds of human mobility. Sci. Rep. 3, (2013), 1–5.

[MobiSys14] Paarijaat Aditya and Peter Druschel. 2014. EnCore : Private, Context-based Communication for Mobile Social Apps. In ACM MobiSys, 135–148.

[KDD14] Gergely Acs and Claude Castelluccia. 2014. A case study: privacy preserving release of spatio-temporal density in Paris. In ACM SIGKDD, 1679–1688.

[INFOCOM14-DLS] Ben Niu, Qinghua Li, Xiaoyan Zhu, Guohong Cao, and Hui Li. 2014. Achieving k-anonymity in privacy-aware location-based services. In IEEE INFOCOM, 754–762.

[SIGSPATIAL15-PLACE] Cyrus Shahabi, Liyue Fan, Luciano Nocera, Li Xiong, and Ming Li. 2015. Privacy-preserving inference of social relationships from location data. In ACM SIGSPATIAL, 1–4.

[WWW17] Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data. WWW, 1241–1250.