Biblio

Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the link between data cleaning and differential privacy in a framework we call PrivateClean. PrivateClean includes a technique for creating private datasets of numerical and discrete-valued attributes, a formalism for privacy-preserving data cleaning, and techniques for answering sum, count, and avg queries after cleaning. We show: (1) how the degree of privacy affects subsequent aggregate query accuracy, (2) how privacy potentially amplifies certain types of errors in a dataset, and (3) how this analysis can be used to tune the degree of privacy. The key insight is to maintain a bipartite graph relating dirty values to clean values and use this graph to estimate biases due to the interaction between cleaning and privacy. We validate these results on four datasets with a variety of well-studied cleaning techniques including using functional dependencies, outlier filtering, and resolving inconsistent attributes.

Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the link between data cleaning and differential privacy in a framework we call PrivateClean. PrivateClean includes a technique for creating private datasets of numerical and discrete-valued attributes, a formalism for privacy-preserving data cleaning, and techniques for answering sum, count, and avg queries after cleaning. We show: (1) how the degree of privacy affects subsequent aggregate query accuracy, (2) how privacy potentially amplifies certain types of errors in a dataset, and (3) how this analysis can be used to tune the degree of privacy. The key insight is to maintain a bipartite graph relating dirty values to clean values and use this graph to estimate biases due to the interaction between cleaning and privacy. We validate these results on four datasets with a variety of well-studied cleaning techniques including using functional dependencies, outlier filtering, and resolving inconsistent attributes.

It is common practice for data scientists to acquire and integrate disparate data sources to achieve higher quality results. But even with a perfectly cleaned and merged data set, two fundamental questions remain: (1) is the integrated data set complete and (2) what is the impact of any unknown (i.e., unobserved) data on query results? In this work, we develop and analyze techniques to estimate the impact of the unknown data (a.k.a., unknown unknowns) on simple aggregate queries. The key idea is that the overlap between different data sources enables us to estimate the number and values of the missing data items. Our main techniques are parameter-free and do not assume prior knowledge about the distribution. Through a series of experiments, we show that estimating the impact of unknown unknowns is invaluable to better assess the results of aggregate queries over integrated data sources.