Proxy-secure computation model: application to k-means clustering implementation, analysis and improvements
Erman Pattuk · Sabanci University · 2010
Distributed privacy preserving data mining applications, where data is divided among several parties, require high amounts of network communication. In order to overcome this overhead, we propose a scheme that reduces remote computations in distributed data mining applications into local computations on a trusted hardware. Cell BE is used to realize the trusted hardware acting as a proxy for the parties. We design a secure two-party computation protocol that can be instrumental in realizing non-colluding parties in privacy-preserving data mining applications. Each party is represented with a signed and encrypted thread on a separate core of Cell BE running in an isolated mode, whereby its execution and data are secured by hardware means. Our implementations and experiments demonstrate that a significant speed up is gained through the new scheme. It is also possible to increase the number of non-colluding parties on Cell BE, which extends the proposed technique to implement most distributed privacy-preserving data mining protocols proposed in literature that require several non-colluding parties.