Private and Secure Distributed Matrix Multiplication Schemes for Replicated or MDS-Coded Servers
IEEE Transactions on Information Forensics and Security（2022）
摘要
In this paper, we study the problem of
private and secure distributed matrix multiplication (PSDMM)
, where a user having a private matrix
$A$
and
$N$
non-colluding servers sharing a library of
$L$
(
$L>1$
) matrices
$B^{(0)}, B^{(1)},\ldots,B^{(L-1)}$
, for which the user wishes to compute
$AB^{(\theta)}$
for some
$\theta \in [0, L$
) without revealing any information of the matrix
$A$
to the servers, and keeping the index
$\theta $
private to the servers. Previous work is limited to the case that the shared library (
i.e.,
the matrices
$B^{(0)}, B^{(1)},\ldots,B^{(L-1)}$
) is stored across the servers in a replicated form and schemes are very scarce in the literature, there is still much room for improvement. In this paper, we propose two PSDMM schemes, where one is limited to the case that the shared library is stored across the servers in a replicated form but has a better performance than state-of-the-art schemes in that it can achieve a smaller recovery threshold and download cost. The other one focuses on the case that the shared library is stored across the servers in an MDS-coded form, which requires less storage in the servers. The second PSDMM code does not subsume the first one even if the underlying MDS code is degraded to a repetition code as they are totally two different schemes.
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关键词
Distributed computation,privacy,secure,distributed matrix multiplication
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