Privacy and accountability for location-based aggregate statistics
By Raluca Ada Popa, Andrew Justin Blumberg, Hari Balakrishnan, Frank H. Li. 2011
110 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Emiliano De Cristofaro
- Dan Boneh
- Henry Corrigan-Gibbs
- Ahmad-Reza Sadeghi
- Andreas Haeberlen
- Daniel Noble
- Xuxian Jiang
- Edo Roth
In Computer Science Applications
Citations in the corpus
- Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “Other privacy guarantees: Many existing systems for collecting sensitive data rely on secret-sharing [18, 44, 50], anonymizing networks [35, 51] or even systems like Tor [42, 63] to aggregate the data privately, but do not explicitly make use of differential privacy.”
- Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, methodology): “The servers can sum up the encrypted bits and decrypt only the final sum [48, 56, 81, 92, 99, 100].”
- Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “In some cases, entities may be interested in only collecting or releasing aggregate location statistics, which, for instance, can be used to calculate the average speed along a road and generate live traffic maps [3, 34], or to estimate the number of people at a restaurant and predict availability a”
- Unsafe exposure analysis of mobile in-app advertisements (2012, background): “PrivStats [29] offers a mechanism so that aggregate location information can be irrefutably collected in a privacy-preserving way.”