Privacy-Preserving Data Publishing

Principal Investigator: Ninghui Li

Funded by National Science Foundation: TC: Small: Provably Private Microdata Publishing. 09/01/2011 - 08/31/2014.

Data are a key resource in today's information age.  The availability of data, however, often causes major privacy threats.  Many data sharing scenarios require data to be anonymized for privacy protection.  Most existing data anonymization techniques, however, satisfy only weak privacy notions that rely on particular assumptions about the adversaries, and provide inadequate protection.  In recent years, the elegant notion of differential privacy has gradually been accepted as the privacy notion of choice for answering statistical queries.  Most research on differential privacy, however, focuses on answering interactive queries, and there are several negative results on publishing microdata while satisfying differential privacy.  Many data sharing scenarios, however, require sharing of microdata.

This project aims at bridging the gap between the elegant notion of differential privacy, and the practical difficulty of publishing microdata while preserving utility.  Building on preliminary results by the PI, which have showed that random sampling plus ``safe'' k-anonymization satisfies differential privacy, this project aims at advancing the state of the art of both the scientific understanding and the techniques for privacy-preserving microdata publishing.  Research activities include developing (1) Practical anonymization methods that can be proven to satisfy differential privacy, while capable of handling high-dimensional data; (2) Relaxations of differential privacy that are more suitable for microdata publishing; (3) Privacy theory and techniques that are easily applied to a family of data sanitization algorithms called localized algorithms, enabling the usage of input perturbation techniques for provably private microdata publishing; (4) Privacy notions and techniques for publishing social network data and network trace data.

 

Personnel

Students: Wahbeh Qadarji Dong Su

Keywords: anonymization, Privacy, privacy preserving