Privacy-preserving inference of social relationships from location data: A vision paper

Cyrus Shahabi, Liyue Fan, Luciano Nocera, Li Xiong, Ming Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Social relationships between people, e.g., whether they are friends with each other, can be inferred by observing their behaviors in the real world. Thanks to the popularity of GPS-enabled mobile devices or online services, a large amount of high-resolution location data becomes available for such inference studies. However, due to the sensitivity of loca- tion data and user privacy concerns, those studies cannot be largely carried out on individually contributed data without privacy guarantees. Furthermore, we observe that the actual location may not be needed for social relationship studies, but rather the fact that two people met and some statistical properties about their meeting locations, which can be com- puted in a private manner. In this paper, we envision an extensible framework, dubbed Privacy-preserving Location Analytics and Computation Environment (PLACE), which enables social relationship studies by analyzing individually generated location data. PLACE utilizes an untrusted server and computes several building blocks to support various so- cial relationship studies, without disclosing location infor- mation to the server and other untrusted parties. We present PLACE with three example social relationship studies which utilize four privacy-preserving blocks with encryption and differential privacy primitives. The successful realization of PLACE will facilitate private location data acquisition from individual devices, thanks to the strong privacy guarantees, and will enable a wide range of applications.

Original languageEnglish (US)
Title of host publication23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
EditorsYan Huang, Mohamed Ali, Jagan Sankaranarayanan, Matthias Renz, Michael Gertz
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450339674
DOIs
StatePublished - Nov 3 2015
Externally publishedYes
Event23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 - Seattle, United States
Duration: Nov 3 2015Nov 6 2015

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume03-06-November-2015

Conference

Conference23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
Country/TerritoryUnited States
CitySeattle
Period11/3/1511/6/15

Keywords

  • Location privacy
  • Social relationship

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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