A Survey of State-of-the-art in Anonymity Metrics
Document Type
Conference Proceeding
Publication Date
10-31-2008
Abstract
Anonymization enables organizations to protect their data and systems from a diverse set of attacks and preserve privacy; however, in the area of anonymized network data, few, if any, are able to precisely quantify how anonymized their information is for any particular dataset. Indeed, recent research indicates that many anonymization techniques leak some information. An ability to confidently measure this information leakage and any changes in anonymity levels plays a crucial role in facilitating the free-flow of cross-organizational network data sharing and promoting wider adoption of anonyimzation techniques. Fortunately, multiple methods of analyzing anonymity exist. Typical approaches use simple quantifications and probabilistic models; however, to the best of our knowledge, only one network data anonymization metric has been proposed. More importantly, no one-stop-shop paper exists that comprehensively surveys this area for other candidate measures; therefore, this paper explores the state-of-the-art of anonymity metrics. The objective is to provide a macro-level view of the systematic analysis of anonymity preservation, degradation, or elimination for data anonymization as well as network communications anonymization. Abstract © ACM.
Source Publication
Proceedings of the 1st ACM workshop on Network data anonymization - NDA '08
Recommended Citation
Douglas J. Kelly, Richard A. Raines, Michael R. Grimaila, Rusty O. Baldwin, and Barry E. Mullins. 2008. A survey of state-of-the-art in anonymity metrics. In Proceedings of the 1st ACM workshop on Network data anonymization (NDA '08). Association for Computing Machinery, New York, NY, USA, 31–40. https://doi.org/10.1145/1456441.1456453
Comments
Copyright © 2008 Association for Computing Machinery.
Co-author D. Kelly was an AFIT PhD student at the time of this paper. (AFIT-DCS-ENG-09-08, March 2009)
The "Link to Full Text" on this page opens the full paper as hosted by the publisher.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted by ACM without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.