Strategies for Graphical Model Selection

Abstract

Research on differential privacy is generally concerned with examining data sets that are static. Because the data sets do not change, every computation on them produces "one-shot" query results; the results do not change aside from random-ness introduced for privacy. There are many circumstances, however, where this model does not apply, or is simply in-feasible. Data streams are examples of non-static data sets where results may change as more data is streamed. Theoretical support for differential privacy with data streams has been researched in the form of differentially private streaming algorithms. In this paper, we present a practical framework for which a non-expert can perform differentially private operations on data streams. The system is built as an extension to PINQ (Privacy Integrated Queries), a differentially private programming framework for static data sets. The streaming extension provides a programmatic interface for the different types of streaming differential privacy from the literature so that the privacy trade-offs of each type of algorithm can be understood by a non-expert programmer.