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1. Uncoded Placement with Linear Sub-Messages for Private Information Retrieval from Storage Constrained Databases

   https://doi.org/10.1109/TCOMM.2020.3010988  

   Woolsey, Nicholas; Chen, Rong-Rong; Ji, Mingyue (July 2020, IEEE Transactions on Communications)

   We propose capacity-achieving schemes for private information retrieval (PIR) from uncoded databases (DBs) with both homogeneous and heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. In general, a PIR scheme is comprised of storage placement and delivery designs. Previous works have derived the capacity, or infimum download cost, of PIR with uncoded storage placement and sufficient conditions of storage placement to meet capacity. However, the currently proposed storage placement designs require splitting each message into an exponential number of sub-messages with respect to the number of DBs. In this work, when DBs have the same storage constraint, we propose two simple storage placement designs that satisfy the capacity conditions. Then, for more general heterogeneous storage constraints, we translate the storage placement design process into a “filling problem”. We design an iterative algorithm to solve the filling problem where, in each iteration, messages are partitioned into sub-messages and stored at subsets of DBs. All of our proposed storage placement designs require a number of sub-messages per message at most equal to the number of DBs. 

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2. The Capacity of Uncoded Storage Constrained PIR

   https://doi.org/10.1109/ISIT.2018.8437729  

   Attia, Mohamed Adel; Kumar, Deepak; Tandon, Ravi (June 2018, 2018 IEEE International Symposium on Information Theory (ISIT))

   Private information retrieval (PIR) allows a user to retrieve a desired message out of K possible messages from N databases (DBs) without revealing the identity of the desired message. In this work, we consider the problem of PIR from uncoded storage constrained DBs. Each DB has a storage capacity of μKL bits, where L is the size of each message in bits, and μ ∈ [1/N, 1] is the normalized storage. In the storage constrained PIR problem, there are two key challenges: a) construction of communication efficient schemes through storage content design at each DB that allow download efficient PIR; and b characterizing the optimal download cost via information-theoretic lower bounds. The novel aspect of this work is to characterize the optimum download cost of PIR with storage constrained DBs for any value of storage. In particular, for any (N, K), we show that the optimal tradeoff between storage (μ) and the download cost (D(μ)) is given by the lower convex hull of the pairs ([t/N](1+[1/t]+[1/(t 2 )]+...+[1/(t K-1 )])) for t = 1,2, ..., N. The main contribution of this paper is the converse proof, i.e., obtaining lower bounds on the download cost for PIR as a function of the available storage. 

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3. PIR from Storage Constrained Databases - Coded Caching Meets PIR

   https://doi.org/10.1109/ICC.2018.8422198  

   Tandon, Ravi; Abdul-Wahid, Maryam; Almoualem, Firas; Kumar, Deepak (May 2018, 2018 IEEE International Conference on Communications (ICC))

   Private information retrieval (PIR) allows a user to retrieve a desired message out of K possible messages from N databases without revealing the identity of the desired message. There has been significant recent progress on understanding fundamental information-theoretic limits of PIR, and in particular the download cost of PIR for several variations. Majority of existing works however, assume the presence of replicated databases, each storing all the K messages. In this work, we consider the problem of PIR from storage constrained databases. Each database has a storage capacity of μKL bits, where K is the number of messages, L is the size of each message in bits, and μ ∈ [1/N, 1] is the normalized storage. In the storage constrained PIR problem, there are two key design questions: a) how to store content across each database under storage constraints; and b) construction of schemes that allow efficient PIR through storage constrained databases. The main contribution of this work is a general achievable scheme for PIR from storage constrained databases for any value of storage. In particular, for any (N, K), with normalized storage μ = t/N, where the parameter t can take integer values t ∈ {1, 2, ..., N}, we show that our proposed PIR scheme achieves a download cost of (1 + 1/t + 1/2 + ⋯ + 1/t K-1 ). The extreme case when μ = 1 (i.e., t = N) corresponds to the setting of replicated databases with full storage. For this extremal setting, our scheme recovers the information-theoretically optimal download cost characterized by Sun and Jafar as (1 + 1/N + ⋯ +1/N K-1 ). For the other extreme, when μ = 1/N (i.e., t = 1), the proposed scheme achieves a download cost of K. The most interesting aspect of the result is that for intermediate values of storage, i.e., 1/N <; μ <; 1, the proposed scheme can strictly outperform memory-sharing between extreme values of storage. 

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4. Cache-aided Multiuser Private Information Retrieval

   https://doi.org/10.1109/ISIT44484.2020.9174083  

   Zhang, Xiang; Wany, Kai; Sunz, Hua; Ji, Mingyue (August 2020, 2020 IEEE International Symposium on Information Theory (ISIT))

   This paper formulates the cache-aided multi-user Private Information Retrieval (MuPIR) problem, including K u cache-equipped users, each of which wishes to retrieve a desired message efficiently from N distributed databases with access to K independent messages. Privacy of the users’ demands requires that any individual database can not learn anything about the demands of the users. The load of this problem is defined as the average number of downloaded bits per desired message bit. The goal is to find the optimal memory-load trade-off while preserving the demand privacy. Besides the formulation of the MuPIR problem, the contribution of this paper is two-fold. First, we characterize the optimal memory-load trade-off for a system with N = 2 databases, K = 2 messages and K u = 2 users demanding distinct messages; Second, a product design with order optimality guarantee is proposed. In addition, the product design can achieve the optimal load when the cache memory is large enough. The product design embeds the well-known Sun-Jafar PIR scheme into coded caching, in order to benefit from the coded caching gain while preserving the privacy of the users’ demands. 

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5. On Single Server Private Information Retrieval With Private Coded Side Information

   https://doi.org/10.1109/TIT.2023.3253078  

   Lu, Yuxiang; Jafar, Syed A. (May 2023, IEEE Transactions on Information Theory)

   Matthieu Bloch (Ed.)

   Motivated by an open problem and a conjecture, this work studies the problem of single server private information retrieval with private coded side information (PIR-PCSI) that was recently introduced by Heidarzadeh et al. The goal of PIR-PCSI is to allow a user to efficiently retrieve a desired message Wθ, which is one of K independent messages that are stored at a server, while utilizing private side information of a linear combination of a uniformly chosen size-M subset (S ⊂ [K]) of messages. The settings PIR-PCSI-I and PIR-PCSI-II correspond to the constraints that θ is generated uniformly from [K]\S, and S, respectively. In each case, (θ, S) must be kept private from the server. The capacity is defined as the supremum over message and field sizes, of achievable rates (number of bits of desired message retrieved per bit of download) and is characterized by Heidarzadeh et al. for PIR-PCSI-I in general, and for PIR- PCSI-II for M > (K + 1)/2 as (K − M + 1)−1. For 2 ≤ M ≤ (K + 1)/2 the capacity of PIR-PCSI-II remains open, and it is conjectured that even in this case the capacity is (K − M + 1)−1. We show the capacity of PIR-PCSI-II is equal to 2/K for 2 ≤ M ≤ K+1, which is strictly larger 2 than the conjectured value, and does not depend on M within this parameter regime. Remarkably, half the side-information is found to be redundant. We also characterize the infimum capacity (infimum over fields instead of supremum), and the capacity with private coefficients. The results are generalized to PIR-PCSI-I (θ ∈ [K] \ S) and PIR-PCSI (θ ∈ [K]) settings. 

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