Towards Practical Homomorphic Time-Lock Puzzles: Applicability and Verifiability

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Abstract

Time-lock puzzle schemes allow one to encrypt messages for the future. More concretely, one can efficiently generate a time-lock puzzle for a secret/solution s, such that s remains hidden until a specified time T has elapsed, even for any parallel adversaries. However, since computation on secrets within multiple puzzles can be performed only when all of these puzzles are solved, the usage of classical time-lock puzzles is greatly limited. Homomorphic time-lock puzzle (HTLP) schemes were thus proposed to allow evaluating functions over puzzles directly without solving them. However, although efficient HTLP schemes exist, more improvements are still needed for practicability. In this paper, we improve HTLP schemes to broaden their application scenarios from the aspects of applicability and verifiability. In terms of applicability, we design the first multiplicatively HTLP scheme with the solution space over Zn∗, which is more expressible than the original one, e.g., representing integers. Then, to fit HTLP into scenarios requiring verifiability that is missing in existing schemes, we propose three simple and fast protocols for both the additively HTLP scheme and our multiplicatively HTLP scheme, respectively. The first two protocols allow a puzzle solver to convince others of the correctness of the solution or the invalidity of the puzzle so that others do not need to solve the puzzle themselves. The third protocol allows a puzzle generator to prove the validity of his puzzles. It is shown that a puzzle in our scheme is only 1.25 KB, and one multiplication on puzzles takes simply 0.01 ms. Meanwhile, the overhead of each protocol is less than 0.6KB in communication and 40 ms in computation. Hence, HTLP still demonstrates excellent efficiency in both communication and computation with these versatile properties.

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APA

Liu, Y., Wang, Q., & Yiu, S. M. (2022). Towards Practical Homomorphic Time-Lock Puzzles: Applicability and Verifiability. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13554 LNCS, pp. 424–443). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-17140-6_21

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