Models and Methods for Privacy Preserving Data Publishing and Analysis Johannes Gehrke   View Paper (PDF)   Return to Invited tutorial 2 Abstract The digitization of our daily lives has led to an explosion in the collection of digital data by governments, corporations, and individuals. Protection of confidentiality of this data is of utmost importance. However, knowledge of statistical properties of this private data can have significant societal benefit, for example, in decisions about the allocation of public funds based on Census data, or in the analysis of medical data from different hospitals to understand the interaction 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 confidentiality. The tutorial will cover definitions of privacy and disclosure, and associated methods how to enforce them. More information, including a list of references to related work can be found at the following website: http://www.cs.cornell.edu/database/privacy. ©2006 Association for Computing Machinery