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Achieving Accuracy and Strong Data Protection Using Differential Privacy

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 Gerome Miklau
Date
Le mardi 02 octobre 2012 à 11:00
Lieu
Salle de Conférence (Pavillon des Jardins)
Orateur
Gerome Miklau (University of Massachusetts, Amherst)

The goal of private statistical data analysis is to permit the accurate analysis of a group of individuals while protecting the personal information of each individual. Differential privacy provides a formal basis for achieving these seemingly contradictory goals. It is a rigorous privacy standard that protects against powerful adversaries, offers precise accuracy guarantees, and has been successfully applied to a range of data analysis tasks. When differential privacy is satisfied, individuals in a dataset enjoy the compelling assurance that information released about the dataset is virtually indistinguishable whether or not their personal data is included.

Differential privacy is achieved by introducing randomness into query answers. A major goal of research in this area is to devise methods that offer the best accuracy for a fixed level of privacy. After reviewing the basic principles of differential privacy, I will describe how query constraints and statistical inference can be used to construct a more accurate differentially-private algorithms, with no privacy penalty. The example application comes from our recent work investigating the properties of a social network that can be studied without threatening the privacy of individuals and their connections. I will show that the degree distribution of a network can be estimated privately and accurately by asking a special query for which constraints are known to hold, and then exploiting the constraints to infer a more accurate final result.


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