Privacy-Preserving Coded Mobile Edge Computing for Low-Latency Distributed Inference
Artikel i vetenskaplig tidskrift, 2022

We consider a mobile edge computing scenario where a number of devices want to perform a linear inference Wx on some local data x given a network-side matrix W. The computation is performed at the network edge over a number of edge servers. We propose a coding scheme that provides information-theoretic privacy against z colluding (honest-but-curious) edge servers, while minimizing the overall latency—comprising upload, computation, download, and decoding latency—in the presence of straggling servers. The proposed scheme exploits Shamir’s secret sharing to yield data privacy and straggler mitigation, combined with replication to provide spatial diversity for the download. We also propose two variants of the scheme that further reduce latency. For a considered scenario with 9 edge servers, the proposed scheme reduces the latency by 8% compared to the nonprivate scheme recently introduced by Zhang and Simeone, while providing privacy against an honestbut-curious edge server.

Data privacy


Spatial diversity

mobile edge computing

Computational modeling




Coded computing


spatial diversity

joint beamforming


Reent Schlegel

Simula UiB

Siddhartha Kumar

Simula UiB

Eirik Rosnes

Simula UiB

Alexandre Graell I Amat

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN) 15580008 (eISSN)

Vol. 40 3 788-799


Elektroteknik och elektronik



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