The Capacity of Private Information Retrieval from Coded Databases
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Abstract (arXiv)
We consider the problem of private information retrieval (PIR) over a distributed storage system. The storage system consists of N non-colluding databases, each storing a coded version of M messages. In the PIR problem, the user wishes to retrieve one of the available messages without revealing the message identity to any individual database. We derive the information-theoretic capacity of this problem, which is defined as the maximum number of bits of the desired message that can be privately retrieved per one bit of downloaded information. We show that the PIR capacity in this case is C=left(1+fracKN+fracK2N2+cdots+fracKM-1NM-1right)-1=(1+Rc+Rc2+cdots+RcM-1)-1=frac1-Rc1-RcM, where Rc is the rate of the (N,K) code used. The capacity is a function of the code rate and the number of messages only regardless of the explicit structure of the storage code. The result implies a fundamental tradeoff between the optimal retrieval cost and the storage cost. The result generalizes the achievability and converse results for the classical PIR with replicating databases to the case of coded databases.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 1.
6 papers.
- 2024Private Counterfactual Retrievalcited by 2
- 2021Single-Server Private Linear Transformation: The Joint Privacy Casecited by 1
- 2022Private Federated Submodel Learning with Sparsificationcited by 0
- 2022Secure Embedding Aggregation for Federated Representation Learningcited by 0
- 2024Private Counterfactual Retrieval With Immutable Featurescited by 0
- 2025What If, But Privately: Private Counterfactual Retrievalcited by 0
- 2025What If, But Privately: Private Counterfactual Retrieval5 Aug 2025
- 2024Private Counterfactual Retrieval With Immutable Features15 Nov 2024
- 2024Private Counterfactual Retrieval17 Oct 2024
- 2022Secure Embedding Aggregation for Federated Representation Learning18 Jun 2022
- 2022Private Federated Submodel Learning with Sparsification31 May 2022
- 2021Single-Server Private Linear Transformation: The Joint Privacy Case9 Jun 2021