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Privacy preservation has emerged in the recent half decade as one of the
.more intriguing aspects of data mining
This is due to both rising concerns about rights violation using data
mining and to the emergence of important
markets (e.g., homeland security, cross company production chain data
.mining) for this type of applications
This area of research is rapidly maturing. Unfortunately, recent studies
all point to one major deficiency -- the lack
of a well defined way of modeling the privacy retained by a privacy
.preserving data mining algorithm
In this work we approach the modeling problem by extending an existing
privacy model -- $k$-anonymity -- which was originally considered
in the context of anonymous communication and then transfered to the
context of data tables releases. We show how this
model can be extended to apply to various models of a data table. Beyond
its immediate contribution for the analysis of the
privacy of practically any data mining model, our extension is also
useful for the development of new data anonymization techniques and
.of new privacy preserving data mining algorithms
:Bio
Ran Wolff is a Technion CS graduate (^Ñ04). He did his post-doc with
.)distributed data mining authority Prof. Hillol Kargupta (UMBC
Previously he has been a summer intern with HP Labs ^Ö Technion. In
addition to peer-to-peer, grid, and sensor network data mining, Ran
publishes on privacy preserving in data mining. His recent work also
regards the use of data mining for grid system management. Ran has
published four journal papers, numerous conference papers, and has
.served on the PC of major data mining conferences