Full Citation
Title: Verifiable differential privacy
Citation Type: Conference Paper
Publication Year: 2015
ISBN: 9781450332385
ISSN:
DOI: 10.1145/2741948.2741978
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Abstract: Working with sensitive data is often a balancing act between privacy and integrity concerns. Consider, for instance, a medical researcher who has analyzed a patient database to judge the effectiveness of a new treatment and would now like to publish her findings. On the one hand, the patients may be concerned that the researcher's results contain too much information and accidentally leak some private fact about themselves; on the other hand, the readers of the published study may be concerned that the results contain too little information, limiting their ability to detect errors in the calculations or flaws in the methodology. This paper presents VerDP, a system for private data analysis that provides both strong integrity and strong differential privacy guarantees. VerDP accepts queries that are written in a special query language, and it processes them only if a) it can certify them as differentially private, and if b) it can prove the integrity of the result in zero knowledge. Our experimental evaluation shows that VerDP can successfully process several different queries from the differential privacy literature, and that the cost of generating and verifying the proofs is practical: for example, a histogram query over a 63,488-entry data set resulted in a 20 kB proof that took 32 EC2 instances less than two hours to generate, and that could be verified on a single machine in about one second.
Url: http://dl.acm.org/citation.cfm?doid=2741948.2741978
User Submitted?: No
Authors: Narayan, Arjun; Feldman, Ariel; Papadimitriou, Antonis; Haeberlen, Andreas
Conference Name: Proceedings of the Tenth European Conference on Computer Systems - EuroSys '15
Publisher Location: Bordeaux, France
Data Collections: IPUMS USA
Topics: Methodology and Data Collection, Other
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