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arXiv 2008.03828cs.ITmath.IT
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双盲 $T$-私有信息检索

Double Blind $T$-Private Information Retrieval

Yuxiang Lu, Zhuqing Jia, Syed A. Jafar

AI总结:

本文提出基于交叉子空间对齐的双盲 $T$-私有信息检索(DB-TPIR)方案,证明其在渐近设置下达到容量,并扩展至 $M$ 路盲 $X$-安全 $T$-私有信息检索(MB-XS-TPIR),实现了多用户多索引下的隐私保护与数据安全检索。

摘要:

双盲 $T$-私有信息检索(DB-TPIR)使得两个用户(各自指定一个索引,分别为 $θ_1, θ_2$)能够从存储所有消息 $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$ 的 $N$ 个服务器集合中,高效检索由这两个索引标记的消息 $W(θ_1,θ_2)$,条件是这两个用户的索引分别对任何多达 $T_1,T_2$ 个串通服务器以及彼此之间保持私有。本文提出了一种基于交叉子空间对齐(cross-subspace alignment)的 DB-TPIR 方案,并证明在大量消息和有界延迟的渐近设置下能够达到容量。该方案随后被扩展到具有多个($M$)索引的 $M$ 路盲 $X$-安全 $T$-私有信息检索(MB-XS-TPIR),每个索引属于不同的用户,每个索引具有任意隐私级别($T_1, T_2,\cdots, T_M$),以及任意级别的数据存储安全性($X$),从而可以高效检索消息 $W(θ_1,θ_2,\cdots, θ_M)$,同时存储数据对多达 $X$ 个串通服务器之间的串通保持安全,第 $m^{th}$ 个用户的索引对多达 $T_m$ 个服务器的串通保持私有,并且每个用户的索引 $θ_m$ 对所有其他用户保持私有。该通用方案依赖于基于张量积的交叉子空间对齐扩展,并且每下载一比特可检索 $1-(X+T_1+\cdots+T_M)/N$ 比特的所需消息。

英文摘要:

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($θ_1, θ_2$, resp.), to efficiently retrieve a message $W(θ_1,θ_2)$ labeled by the two indices, from a set of $N$ servers that store all messages $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$, such that the two users' indices are kept private from any set of up to $T_1,T_2$ 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 $M$-way blind $X$-secure $T$-private information retrieval (MB-XS-TPIR) with multiple ($M$) indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots, T_M$), and arbitrary level of security ($X$) of data storage, so that the message $W(θ_1,θ_2,\cdots, θ_M)$ can be efficiently retrieved while the stored data is held secure against collusion among up to $X$ colluding servers, the $m^{th}$ user's index is private against collusion among up to $T_m$ servers, and each user's index $θ_m$ is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves $1-(X+T_1+\cdots+T_M)/N$ bits of desired message per bit of download.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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