Using secure coprocessors for privacy preserving collaborative data mining and analysis

Authors:
Bishwaranjan Bhattacharjee;Naoki Abe;Kenneth Goldman;Bianca Zadrozny;Vamsavardhana R. Chillakuru;Marysabel del Carpio;Chid Apte
Affiliations:
IBM T. J. Watson Research Center;IBM T. J. Watson Research Center;IBM T. J. Watson Research Center;Universidade Federal Fluminense;IBM;IBM;IBM T. J. Watson Research Center
Venue:
DaMoN '06 Proceedings of the 2nd international workshop on Data management on new hardware
Year:
2006

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Abstract

Secure coprocessors have traditionally been used as a keystone of a security subsystem, eliminating the need to protect the rest of the subsystem with physical security measures. With technological advances and hardware miniaturization they have become increasingly powerful. This opens up the possibility of using them for non traditional use. This paper describes a solution for privacy preserving data sharing and mining using cryptographically secure but resource limited coprocessors. It uses memory light data mining methodologies along with a light weight database engine with federation capability, running on a coprocessor. The data to be shared resides with the enterprises that want to collaborate. This system will allow multiple enterprises, which are generally not allowed to share data, to do so solely for the purpose of detecting particular types of anomalies and for generating alerts. We also present results from experiments which demonstrate the value of such collaborations.