Achievable Rate of Private Function Retrieval from MDS Coded Databases

By Sarah A. Obead, Jörg Kliewer. 2018

31 citations recorded by the source corpus.

We study the problem of private function retrieval (PFR) in a distributed storage system. In PFR the user wishes to retrieve a linear combination of $M$ messages stored in non-colluding (N, K) MDS coded databases while revealing no information about the coefficients of the intended linear combination to any of the individual databases. We present an achievable scheme for MDS coded PFR with a rate that matches the capacity for coded private information retrieval derived recently, $R= (1+R_{c}+R_{c}^{2}+\cdots +R_{c}^{M-1})^{-1}=\frac{1-R_{c}}{1-R_{c}^{M}}$, where $R_{c}=\frac{K}{N}$ is the rate of the MDS code.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

On the Upload versus Download Cost for Secure and Private Matrix Multiplication (2019, background): “In particular, starting from the work of [13], private information retrieval (PIR) has been studied extensively within an information theoretic framework (such as PIR from coded databses [14] and private computation [15]–[18]).”
The Capacity of Private Computation (2017, background): “…will focus primarily on this basic setting which opens the door to numerous other open problems through various generalizations (some of which have appeared recently [21]–[24]), e.g., coded storage instead of replication, colluding servers, symmetric privacy requirements, non-linear functions, etc.”