ITR: Private Prediction Using Selective Models

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This research will study the SPMP (Selective Private Model-basedPrediction) problem demonstrated with the following scenario: A clientwith a private input wants to use a server's private model to makepredictions; however, neither side wants to disclose its private data toanybody. More specifically, the research will study the problem forselected models including the Hidden Markov Model, the Neural NetworkModel, the Bayesian Network Model, and the Decision Trees Model. The goalis to develop efficient and practical solutions that enable such type ofprivacy preserving prediction.The project will investigate two approaches: commodity-server approach andmultiple-server approach. The commodity-server approach uses thecommodities (data) from a third party to preserve the confidentiality ofthe private data. The multiple-server approach uses duplicate servers sono single server can learn all information about the client's data. Basedon these two approaches, various data disguising techniques will also bestudied.Efficient solutions to the SPMP problems enable model owners to providenew forms of e-commerce services while protecting customers' privateinformation. Furthermore, the results, methodologies, and the buildingblocks gained from the proposed activity can provide invaluableunderstanding and insights into the Secure Multiparty Computation (SMC)research, and help to advance and expand the areas of SMC research.---------------------------------------