Private Noisy Side Information Helps to Increase the Capacity of SPIR

Abstract

Noiseless private side information does not reduce the download cost in Symmetric Private Information Retrieval (SPIR) unless the client knows all but one file. While this is a pessimistic result, we explore in this paper whether noisy client side information available at the client helps decrease the download cost in the context of SPIR with colluding and replicated servers. Specifically, we assume that the client possesses noisy side information about each stored file, which is obtained by passing each file through one of D possible discrete memoryless test channels. The statistics of the test channels are known by the client and by all the servers, but the mapping $\boldsymbol {\mathcal {M}}$ between the files and the test channels is unknown to the servers. We study this problem under two privacy metrics. Under the first metric, the client wants to preserve the privacy of its file selection and the mapping $\boldsymbol {\mathcal {M}}$ , and the servers want to preserve the privacy of all the non-selected files. Under the second metric, the client is willing to reveal the index of the test channel that is associated with its desired file. For both privacy metrics, we derive the optimal common randomness and download cost. Our setup generalizes SPIR with colluding servers and SPIR with private noiseless side information. Unlike noiseless side information, our results demonstrate that noisy side information can reduce the download cost, even when the client does not have noiseless knowledge of all but one file.