Pufferfish Privacy Mechanisms for Correlated Data

By Shuang Song, Yizhen Wang, Kamalika Chaudhuri. 2016

172 citations recorded by the source corpus.

Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data. This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.

Subfields: Artificial Intelligence, Sociology and Political Science

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

1. Shimei Pan
2. James R. Foulds
3. Yu-Xiang Wang
4. Olga Russakovsky
5. Angelina Wang
6. Jaume Amores

In Sociology and Political Science

1. Shimei Pan
2. James R. Foulds
3. Olga Russakovsky
4. Angelina Wang

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