Limiting privacy breaches in privacy preserving data mining

There has been increasing interest in the problem of building accurate data mining models over aggregate data, while protecting privacy at the level of individual records. One approach for this problem is to randomize the values in individual records, and only disclose the randomized values. The model is then built over the randomized data, after first compensating for the randomization (at the aggregate level). This approach is potentially vulnerable to privacy breaches: based on the distribution of the data, one may be able to learn with high confidence that some of the randomized records satisfy a specified property, even though privacy is preserved on average. In this paper, we present a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them. Unlike earlier approaches, amplification makes it is possible to guarantee limits on privacy breaches without any knowledge of the distribution of the original data. We instantiate this ...

• Type: Conference
• Year: 2003
• Where: PODS
• Authors: Alexandre V. Evfimievski, Johannes Gehrke, Ramakrishnan Srikant
• Keywords: Database, Mining Association Rules, PODS 2003, Privacy Breaches, Randomized Data