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We consider an edge computing scenario where users want to perform a linearcomputation on local, private data and a network-wide, public matrix. Usersoffload computations to edge servers located at the edge of the network, but donot want the servers, or any other party with access to the wireless links, togain any information about their data. We provide a scheme that guaranteesinformation-theoretic user data privacy against an eavesdropper with access toa number of edge servers or their corresponding communication links. Thenovelty of the proposed scheme lies in the utilization of secret sharing andpartial replication to provide privacy, mitigate the effect of stragglingservers, and to allow for joint beamforming opportunities in the downloadphase, to minimize the overall latency, consisting of upload, computation, anddownload latencies.

Private Edge Computing for Linear Inference Based on Secret Sharing