Private Edge Computing for Linear Inference Based on Secret Sharing
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We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. The users offload computations to edge servers located at the edge of the network, but do not want the servers, or any other party with access to the wireless links, to gain any information about their data. We provide a scheme that guarantees information-theoretic user data privacy against an eavesdropper with access to a number of edge servers or their corresponding communication links. The proposed scheme utilizes secret sharing and partial replication to provide privacy, mitigate the effect of straggling servers, and to allow for joint beamforming opportunities in the download phase, in order to minimize the overall latency, consisting of upload, computation, and download latencies.
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