2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | P.11.5 | ||
| Paper Title | Uplink Cost Adjustable Schemes in Secure Distributed Matrix Multiplication | ||
| Authors | Jaber Kakar, Anton Khristoforov, Seyedhamed Ebadifar, Aydin Sezgin, Ruhr University Bochum, Germany | ||
| Session | P.11: Topics in Privacy and Cryptography | ||
| Presentation | Lecture | ||
| Track | Cryptography, Security and Privacy | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | In secure distributed matrix multiplication (SDMM) the multiplication $\bm A\bm B$ from two private matrices $\bm A$ and $\bm B$ is outsourced by a user to $N$ distributed servers. In $\ell$-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both $\bm A$ and $\bm B$ to any set of $\ell\leq N$ servers. To this end, the user applies coding with $\tilde{\bm A}_i$ and $\tilde{\bm B}_i$ representing encoded versions of $\bm A$ and $\bm B$ destined to the $i$-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between uplink (UL) and downlink (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We implement schemes from the literature, in addition to USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spend on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of $\left(\tilde{\bm A}_i,\tilde{\bm B}_i\right)_{i=1}^{N}$ and the computation of $\bm O_i=\tilde{\bm A}_i\tilde{\bm B}_i$, with those of SCSA, being a low timing overhead for the download of $\left(\bm O_i\right)_{i=1}^{N}$ and the decoding of $\bm A\bm B$. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
