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SIGMOD
2012
ACM

GUPT: privacy preserving data analysis made easy

6 years 1 months ago
GUPT: privacy preserving data analysis made easy
It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data risks leaking information through its output. Differential privacy provides a theoretical framework for processing data while protecting the privacy of individual records in a dataset. Unfortunately, it has seen limited adoption because of the loss in output accuracy, the difficulty in making programs differentially private, lack of mechanisms to describe the privacy budget in a programmer’s utilitarian terms, and the challenging requirement that data owners and data analysts manually distribute the limited privacy budget between queries. This paper presents the design and evaluation of a new system, GUPT, that overcomes these challenges. Unlike existing differentially private systems such as PINQ and Airavat, it guarantees differential privacy to programs not developed with privacy in mind, makes no trust assumptions abou...
Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, D
Added 27 Sep 2012
Updated 27 Sep 2012
Type Journal
Year 2012
Where SIGMOD
Authors Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, David E. Culler
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