Agrawal, Rakesh and Srikant, Ramakrishnan and Thomas, Dilys (2005) Privacy Preserving OLAP. In: 24th ACM International Conference on Management of Data (SIGMOD 2005), June 14-16, 2005, Baltimore, Maryland.
We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the server. We develop formal notions of privacy obtained from data perturbation and show that our perturbation provides guarantees against privacy breaches.We develop and analyze algorithms for reconstructing counts of subcubes over perturbed data. We also evaluate the tradeoff between privacy guarantees and reconstruction accuracy and show the practicality of our approach.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Data Privacy, Data Mining, OLAP, Privacy Preserving Analytics|
|Subjects:||Computer Science > Data Mining|
|Related URLs:||Project Homepage||http://crypto.stanford.edu/portia/|
|Deposited By:||Import Account|
|Deposited On:||05 Jun 2006 17:00|
|Last Modified:||22 Dec 2008 17:40|
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