Welcome to the resource topic for 2022/1362
Title:
ALLOSAUR: Accumulator with Low-Latency Oblivious Sublinear Anonymous credential Updates with Revocations
Authors: Samuel Jaques, Michael Lodder, Hart Montgomery
Abstract:A cryptographic accumulator is a space- and time-efficient data structure with associated algorithms used for secure membership testing. In the growing space of digital credentials, accumulators found in managing a set of valid credentials, giving efficient and anonymous methods for credential holders to prove their validity. Unlike traditional credentials like digital signatures, one can easily revoke credentials with an accumulator; however, each revocation forces existing credential holders to engage in an expensive update process. Previous works make this faster and easier by sacrificing anonymity. To improve performance without compromising privacy, we present ALLOSAUR, a multi-party accumulator based on pairings. In ALLOSAUR, we eliminate the cost of accumulating new credentials, let “credential managers” manage the accumulator values with secure multiparty computation, and allow anonymous credential updates with a square-root reduction in communication costs as compared to existing work.
A deployed digital credential system is a vast and complicated system, and existing formalisms do not fully address the scope or power of a real-world adversary. We develop a thorough UC-style formalism that allows arbitrary malicious behaviour from an adversary controlling a minority of credential managers and arbitrary numbers of users, credentials, and verifiers. In our new formalism we present a novel definition of privacy that captures as much anonymity as possible while accounting for inevitable losses from interaction with the system. The detail in our formalism reveals real-world issues in existing accumulator constructions, all of which ALLOSAUR avoids.
Our proof-of-concept implementation can update over 1000 revocations with less than half a second of total computation and 16 kB communication, at least a 5x improvement over the previous state-of-the-art in both metrics.
ePrint: https://eprint.iacr.org/2022/1362
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