• Calendar

December 27, Tuesday
12:00 – 14:00

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 (04). He did his post-doc with distributed data mining authority Prof. Hillol Kargupta (UMBC). Previously he has been a summer intern with HP Labs 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.