Privacy-Preserving Data Publishing : Where Are We Now?

The massive personal datasets collected by today's companies or institutions are valuable resources, both for the entities that hold them and for society at large. Privacy-preserving data publishing aims at opening personal datasets to large-scale analysis without jeopardizing the individuals privacy. The problem is hard, ranging from the definition of an adequate privacy criteria to the design of efficient and useful privacy algorithms. More than ten years after the publications of the two seminal l-Diversity and Differential Privacy works, this lecture is a guided tour of the main privacy-preserving data publishing models and algorithms. We will synthesize the partition-based and differential privacy families of models and algorithms, analyze their strengths and weaknesses, and try to extract strong tendencies from the past decade. We will conclude by focusing on a recent line of work that intertwins differential privacy with encryption for performing analytical algorithms in a distributed and private way.

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