Double Blind <i>T</i>-Private Information Retrieval

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($\theta_1, \theta_2$, resp.), to efficiently retrieve a message $W(\theta_1,\theta_2)$ labeled by the indices, from set $N$ 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 users' indices are kept private any up $T_1,T_2$ colluding servers, respectively, as well other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown be capacity-achieving asymptotic setting large number bounded latency. The then extended $M$-way $X$-secure (MB-XS-TPIR) with multiple ($M$) belonging different user, arbitrary privacy levels for ($T_1, T_2,\cdots, T_M$), level security ($X$) data storage, so $W(\theta_1,\theta_2,\cdots, \theta_M)$ can retrieved while stored held secure against collusion among $X$ $m^{th}$ user's $T_m$ $\theta_m$ other users. general relies tensor-product extension retrieves $1-(X+T_1+\cdots+T_M)/N$ bits desired per bit download.