Adaptive Differentially Private Data Release (ADP)

Current information technology enables many organizations to collect, store, and use massive amount and various types of information about individuals. Governments and other organizations increasingly recognize the critical value and enormous opportunities in sharing such a wealth of information. However data privacy has been a major barrier for such information sharing, bringing much attention to privacy preserving data publishing and analysis techniques. Differential privacy is widely accepted as one of the strongest privacy guarantees. While many effective mechanisms have been proposed for the interactive model, non-interactive data release with differential privacy while maintaining data utility remains an open problem.

The Adaptive Differentially Private Data Release (ADP) project aims to build a data-driven and adaptive framework for differentially private data release. It circumvents the hardness of differentially private data release in the non-interactive setting by novel and sophisticated use of the differentially private primitives exploiting the characteristics of the underlying data. The specific research objectives include:

(1) design adaptive strategies for releasing data with differential privacy, including traditional relational data, high dimensional and sparse set-valued data, and time series data,

(2) design statistical inference techniques to accurately answer user queries using previously released data or query answers and derive utility bounds,

(3) design algorithms to model and incorporate potentially dynamic workload characteristics.