JOURNAL ARTICLE

Private information retrieval from MDS coded data in distributed storage systems

Abstract

We consider the problem of providing privacy, in the private information retrieval (PIR) sense, to users requesting data from a distributed storage system (DSS). The DSS uses an (n, k) Maximum Distance Separable (MDS) code to store the data reliably on unreliable storage nodes. Some of these nodes can be spies which report to a third party, such as an oppressive regime, which data is being requested by the user. An information theoretic PIR scheme ensures that a user can satisfy its request while revealing, to the spy nodes, no information on which data is being requested. A user can achieve PIR by downloading all the data in the DSS. However, this is not a feasible solution due to its high communication cost. We construct PIR schemes with low download communication cost. When there is b = 1 spy node in the DSS, we construct PIR schemes with download cost 1/1−R per unit of requested data (R = k/n is the code rate), achieving the information theoretic limit for linear schemes. The proposed schemes are universal since they depend on the code rate, but not on the generator matrix of the code. When there are 2 ≤ b ≤ n − k spy nodes, we devise linear PIR schemes that have download cost equal to b + k per unit of requested data.

Keywords:
Computer science Upload Download Construct (python library) Code (set theory) Distributed data store Node (physics) Private information retrieval Computer network Scheme (mathematics) Separable space Generator matrix Algorithm Computer security Decoding methods Operating system Mathematics Set (abstract data type)

Metrics

86
Cited By
21.70
FWCI (Field Weighted Citation Impact)
18
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cryptography and Data Security
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Data Storage Technologies
Physical Sciences →  Computer Science →  Computer Networks and Communications
Privacy-Preserving Technologies in Data
Physical Sciences →  Computer Science →  Artificial Intelligence

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