Privacy and accountability for location-based aggregate statistics
Name
Balakrishnan-Privacy and accountability.pdf
Size
553.47 KB
Format
Adobe PDF
Checksum (MD5)
d7c02c9bb64a97d4c70ca7c016fb817d
Author(s)
Popa, Raluca Ada
Blumberg, Andrew J.
Balakrishnan, Hari
Li, Frank H.
Date Issued
October 2011
Journal
Proceedings of the 18th ACM conference on Computer and communications security (CCS '11)
Publisher
Association for Computing Machinery (ACM)
Citation
Raluca Ada Popa, Andrew J. Blumberg, Hari Balakrishnan, and Frank H. Li. 2011. Privacy and accountability for location-based aggregate statistics. In Proceedings of the 18th ACM conference on Computer and communications security (CCS '11). ACM, New York, NY, USA, 653-666. DOI=10.1145/2046707.2046781 http://doi.acm.org/10.1145/2046707.2046781
Version
Author's final manuscript
Abstract
A significant and growing class of location-based mobile applications aggregate position data from individual devices at a server and compute aggregate statistics over these position streams. Because these devices can be linked to the movement of individuals, there is significant danger that the aggregate computation will violate the location privacy of individuals. This paper develops and evaluates PrivStats, a system for computing aggregate statistics over location data that simultaneously achieves two properties: first, provable guarantees on location privacy even in the face of any side information about users known to the server, and second, privacy-preserving accountability (i.e., protection against abusive clients uploading large amounts of spurious data). PrivStats achieves these properties using a new protocol for uploading and aggregating data anonymously as well as an efficient zero-knowledge proof of knowledge protocol we developed from scratch for accountability. We implemented our system on Nexus One smartphones and commodity servers. Our experimental results demonstrate that PrivStats is a practical system: computing a common aggregate (e.g., count) over the data of 10,000 clients takes less than 0.46 s at the server and the protocol has modest latency (0.6 s) to upload data from a Nexus phone. We also validated our protocols on real driver traces from the CarTel project.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2046707.2046781
