Privacy-Preserving Classifier Learning

Authors:
Justin Brickell;Vitaly Shmatikov
Affiliations:
The University of Texas at Austin, Austin, USA 78712;The University of Texas at Austin, Austin, USA 78712
Venue:
Financial Cryptography and Data Security
Year:
2009

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Abstract

We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database held by a remote server without learning any additional information about the records held in the database. The server does not learn anything about the constructed classifier, not even the user's choice of feature and class attributes.Our protocol uses several novel techniques to enable oblivious classifier construction. We evaluate a prototype implementation, and demonstrate that its performance is efficient for practical scenarios.