How to Sanitize Data?


Citation

Paper

Bibliographic Information

Abstract

Balancing the needs of a data analyst with the privacy needs of a data provider is a key issue when data is sanitized. We treat both the requirements of the analyst and the privacy expectations as policies, and compose the two policies to detect conflicts. The result can be applied to an intermediate data representation to sanitize the relevant pans of the data. We conclude that this method has promise, but more work is needed to determine its effectiveness and limits.

Copyright Notice

©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
The definitive version was published in Proceedings of the 13th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprise, June 2004.