ESTIMATING ACCURACY OF PRIVACY-PRESERVING DATA ANALYSES

摘要

The present invention relates to systems and methods for estimating the accuracy, in the form of confidence intervals, of data released under Differential Privacy (DP) mechanisms and their aggregation. Reasoning about the accuracy of aggregated released data can be improved by combining the use of probabilistic bounds like union and Chernoff bounds. Some probabilistic bounds, e.g., Chernoff bound, rely on detecting statistical independence of random variables, which in this case corresponds to sources of statistical noise of DP mechanisms. To detect such independence, and provide accuracy calculations, provenance of statistical noise sources as well as information flows of random variables are tracked within data analyses, i.e., where, within data analyses, randomly generated statistical noise propagates and how it gets manipulated.