The Capacity of Robust Private Information Retrieval with Colluding Databases
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Abstract (arXiv)
Private information retrieval (PIR) is the problem of retrieving as efficiently as possible, one out of K messages from N non-communicating replicated databases (each holds all K messages) while keeping the identity of the desired message index a secret from each individual database. The information theoretic capacity of PIR (equivalently, the reciprocal of minimum download cost) is the maximum number of bits of desired information that can be privately retrieved per bit of downloaded information. T-private PIR is a generalization of PIR to include the requirement that even if any T of the N databases collude, the identity of the retrieved message remains completely unknown to them. Robust PIR is another generalization that refers to the scenario where we have M ≥ N databases, out of which any M - N may fail to respond. For K messages and M≥ N databases out of which at least some N must respond, we show that the capacity of T-private and Robust PIR is left(1+T/N+T2/N2+cdots+TK-1/NK-1right)-1. The result includes as special cases the capacity of PIR without robustness (M=N) or T-privacy constraints (T=1).
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 May 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1.
6 papers.
- 2023Adversarial Attacks on Online Learning to Rank with Stochastic Click Modelscited by 6
- 2024Private Counterfactual Retrievalcited by 2
- 2021Single-Server Private Linear Transformation: The Joint Privacy Casecited by 1
- 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
- 2024Private Counterfactual Retrieval17 Oct 2024
- 2023Adversarial Attacks on Online Learning to Rank with Stochastic Click Models30 May 2023
- 2022Private Federated Submodel Learning with Sparsification31 May 2022
- 2021Single-Server Private Linear Transformation: The Joint Privacy Case9 Jun 2021