Privacy-Preserving Outlier Detection

Jaideep Vaidya
Rutgers University

Monday, 2 May, 2:00PM
Burchard 124
Computer Science Department
Stevens Institute of Technology
 

Abstract


Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The problem lies not so much with the results of data mining, but rather with the process of data mining. Current data mining algorithms require some form of access to all of the data, which in and of itself provides oppurtunity for misuse.

The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. Related work in cryptography provides a strong theoretical foundation for secure computation. Cryptographic approaches to preserving privacy enable formal guarantees for privacy preservation.

This talk provides a brief introduction to the area as well as a synopsis of methods for secure outlier detection in this context. We present solutions for both homogeneous as well as heterogeneous distribution of data. At the end of the protocol, all parties learn the outliers in the global data set, without learning any other information. This work was presented at IEEE ICDM '04.