Interval Approach to Preserving Privacy in Statistical Databases: Related Challenges and Algorithms of Computational Statistics

Authors

Luc Longpre, The University of Texas at El PasoFollow
Gang Xiang
Vladik Kreinovich, The University of Texas at El PasoFollow
Eric Freudenthal, The University of Texas at El PasoFollow

Publication Date

3-2007

Comments

Technical Report: UTEP-CS-07-17a

Published in: V. Gorodetsky, I. Kotenko, and V. A. Skormin (eds.), Proceedings of the International Conference "Mathematical Methods, Models and Architectures for Computer Networks Security" MMM-ACNS-07, St. Petersburg, Russia, September 13-15, 2007, Springer Lecture Notes in Computer Science, 2007, Vol. CCIS-1, pp. 346-361.

Abstract

In many practical situations, it is important to store large amounts of data and to be able to statistically process the data. A large part of the data is confidential, so while we welcome statistical data processing, we do not want to reveal sensitive individual data. If we allow researchers to ask all kinds of statistical queries, this can lead to violation of people's privacy. A sure-proof way to avoid these privacy violations is to store ranges of values (e.g., between 40 and 50 for age) instead of the actual values. This idea solves the privacy problem, but it leads to a computational challenge: traditional statistical algorithms need exact data, but now we only know data with interval uncertainty. In this paper, we describe new algorithms designed for processing such interval data.