More is Merrier: Relax the Non-Collusion Assumption in Multi-Server PIR
Tiantian Gong, Ryan Henry, Alexandros Psomas, Aniket Kate
Abstract
A long line of research on secure computation has confirmed that anything that can be computed, can be computed securely using a set of non-colluding parties. Indeed, this non-collusion assumption makes a number of problems solvable, as well as reduces overheads and bypasses computational hardness results, and it is pervasive across different privacy-enhancing technologies. However, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation task, collusion attempts in privacy-preserving computations could be deterred.We focus on the prominent privacy-preserving computation task of multi-server 1-private information retrieval (PIR) that inherently assumes no pair-wise collusion. For PIR application scenarios, such as those for blockchain light clients, where the available servers can be plentiful, a single server’s deviating action is not tremendously beneficial to itself. We can make deviations undesired via small amounts of rewards and penalties, thus significantly raising the bar for collusion resistance. We design and implement a collusion mitigation mechanism on a public bulletin board with payment execution functions, considering only rational and malicious parties with no honest non-colluding servers. Privacy protection is offered for an extended period after the query executions.
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Cited by top-tier papers4
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- UnifOMR: Oblivious Message Retrieval with Near-optimal Concrete EfficiencyBen Fisch, Zeyu Liu, Eran Tromer, Yunhao WangCCS 2026
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- CrypTen: Secure Multi-Party Computation Meets Machine LearningBrian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta et al.NeurIPS 2021 · 573 citations
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