Basser Seminar Series

Privacy Preserving Data Mining

Speaker: Dr Arik Friedman
Technion - Israel Institute of Technology

Time: Friday 5 August 2011, 4:00-5:00pm
Refreshments will be available from 3:30pm
Location: The University of Sydney, School of IT Building, Lecture Theatre (Room 123), Level 1

Add seminar to my diary

Abstract

In recent years the data mining community has faced a new challenge. Having shown how effective its tools are in revealing the knowledge locked within huge databases, it is now required to develop methods that restrain the power of these tools to protect the privacy of individuals. This talk focuses on the problem of guaranteeing privacy of data mining output. We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples. This is joint work with Assaf Schuster. It appeared in KDD'10.

This talk will be self-contained and no background in privacy or data mining will be presumed.

Speaker's biography

Arik Friedman has recently finished his PhD at the Technion, Israel Institute of Technology. His research interests include privacy, computer security, data mining and machine learning, and how all the above can be combined. He completed his BA (Summa Cum Laude) in computer science at the Technion, and he also holds an MBA (Magna Cum Laude) from Tel-Aviv University. In the last few years Arik has combined his academic studies with a program management position in Microsoft. In this role he worked on security and privacy related products and put the theory into practice.