Double Blind $T$-Private Information Retrieval
Yuxiang Lu, Zhuqing Jia, Syed A. Jafar

TL;DR
This paper introduces a capacity-achieving scheme for double blind T-private information retrieval, enabling two users to privately retrieve messages from multiple servers, with extensions to multi-user, secure, and privacy levels.
Contribution
It proposes a novel cross-subspace alignment scheme for double blind T-private retrieval and extends it to multi-user, secure, and privacy settings with arbitrary parameters.
Findings
Achieves capacity in large message regimes.
Extends to multi-user, secure, and privacy settings.
Retrieves a fraction of desired message per download.
Abstract
Double blind -private information retrieval (DB-TPIR) enables two users, each of whom specifies an index (, resp.), to efficiently retrieve a message labeled by the two indices, from a set of servers that store all messages , such that the two users' indices are kept private from any set of up to colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to -way blind -secure -private information retrieval (MB-XS-TPIR) with multiple () indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots,…
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