Multi-Message Private Information Retrieval: Capacity Results and Near-Optimal Schemes
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Abstract (arXiv)
We consider the problem of multi-message private information retrieval (MPIR) from N non-communicating replicated databases. In MPIR, the user is interested in retrieving P messages out of M stored messages without leaking the identity of the retrieved messages. The information-theoretic sum capacity of MPIR CsP is the maximum number of desired message symbols that can be retrieved privately per downloaded symbol. For the case P ≥ fracM2, we determine the exact sum capacity of MPIR as CsP=frac11+fracM-PPN. The achievable scheme in this case is based on downloading MDS-coded mixtures of all messages. For P ≤ fracM2, we develop lower and upper bounds for all M,P,N. These bounds match if the total number of messages M is an integer multiple of the number of desired messages P, i.e., fracMP ∈ mathbbN. In this case, CsP=frac1-frac1N1-(frac1N)M/P. The achievable scheme in this case generalizes the single-message capacity achieving scheme to have unbalanced number of stages per round of download. For all the remaining cases, the difference between the lower and upper bound is at most 0.0082, which occurs for M=5, P=2, N=2. Our results indicate that joint retrieval of desired messages is more efficient than successive use of single-message retrieval schemes.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Single-Server Private Linear Transformation: The Joint Privacy Casecited by 1
- 2021Single-Server Private Linear Transformation: The Individual Privacy Casecited by 0
- 2022Private Federated Submodel Learning with Sparsificationcited 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
- 2022Private Federated Submodel Learning with Sparsification31 May 2022
- 2021Single-Server Private Linear Transformation: The Individual Privacy Case9 Jun 2021
- 2021Single-Server Private Linear Transformation: The Joint Privacy Case9 Jun 2021