Private Edge Computing for Linear Inference Based on Secret Sharing
Paper i proceeding, 2020

We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. 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 novelty of the proposed scheme lies in the utilization of 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, to minimize the overall latency, consisting of upload, computation, and download latencies.


Reent Schlegel

Simula UiB

Siddhartha Kumar

Simula UiB

Eirik Rosnes

Simula UiB

Alexandre Graell I Amat

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Vol. 2020-January 9348077

2020 IEEE Global Communications Conference, GLOBECOM 2020
Virtual, Taipei, Taiwan,







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