Models and Methods for Privacy-Preserving Data Analysis and Publishing

Johannes GehrkeDepartment of Computer Science

Cornell UniversityIthaca, NY, 14850

johannes@cs.cornell.edu

Abstract

The digitization of our daily lives has led to an explosion

in the collection of data by governments, corporations, and

individuals. Protection of confidentiality of this data is of

utmost importance. However, knowledge of statistical prop-

erties of this private data can have significant societal ben-

efit, for example, in decisions about the allocation of pub-

lic funds based on Census data, or in the analysis of medi-

cal data from different hospitals to understand the interac-

tion of drugs. This tutorial will survey recent research that

builds bridges between the two seemingly conflicting goals

of sharing data while preserving data privacy and confi-

dentiality. The tutorial will cover definitions of privacy and

disclosure, and associated methods how to enforce them.

More information, including a list of references to re-

lated work can be found at the following website:

http://www.cs.cornell.edu/database/privacy.

Proceedings of the 22nd International Conference on Data Engineering (ICDE’06) 8-7695-2570-9/06 $20.00 © 2006 IEEE