Adaptive Differentially Private Data Release for Data Sharing and Data Mining

Summary form only given. Current information technology enables many organizations to collect, store, and use massive amount and various types of information about individuals. While sharing such a wealth of information presents enormous opportunities for data mining applications, data privacy has been a major barrier. Differential privacy is widely accepted as one of the strongest privacy guarantees. While many effective mechanisms have been proposed for specific data mining applications, non-interactive data release to support exploratory data analysis with differential privacy remains an open problem. I will present our Adaptive Differentially Private Data Release (ADP) project which aims to build a suite of data-driven and adaptive techniques for differentially private data release by exploiting the characteristics of the underlying data. I will present our ongoing work on techniques for handling different types of data including relational, high dimensional, transactional, sequential, and time series data. I will present case studies using real datasets demonstrating the feasibility of using the released data for various data mining tasks such as classification and frequent pattern mining. Finally, I will discuss the challenges and open questions of applying the differential privacy framework for general data sharing.