Provably Secure Proactive Secret Sharing Without the Adjacent Assumption

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Abstract

In secret sharing (SS), the secret is shared among a number of parties so that only a quorum of these parties can recover the secret, but a smaller set of parties cannot learn any information about the secret. However, the traditional SS technique is insufficient to protect the secret with a long lifetime, because the adversary may gradually compromise enough parties to retrieve the secret over the long time. To solve this issue, proactive secret sharing (PSS) divides the lifetime of the secret into many short time periods and the parties jointly update their secret shares in each time period. The benefit is that if the adversary cannot break into enough parties in a single time period, her compromised shares will become obsolete after the shares being updated. In the last two decades, many PSS schemes have been proposed and they are widely used in various security protocols. However, the majority of existing PSS schemes require the adjacent assumption, i.e. if a party is corrupted during an update phase, it is corrupted in both time periods adjacent to that update phase. Note that this assumption not only hinders the security model to capture the mobile adversary’s abilities, but also prevents PSS schemes being used in many real-world applications. In this paper, we revisit the research of PSS, and our work contributes in the following aspects. Firstly, we discuss why some existing schemes (including Herzberg’s PSS scheme) cannot maintain their security when the adjacent assumption is removed. Secondly, we use the polynomial truncation method to improve Herzberg’s PSS scheme. To the best of our knowledge, our proposed scheme is the first provably secure PSS scheme without the adjacent assumption.

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APA

Xia, Z., Yang, B., Zhou, Y., Zhang, M., Shen, H., & Mu, Y. (2019). Provably Secure Proactive Secret Sharing Without the Adjacent Assumption. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11821 LNCS, pp. 247–264). Springer. https://doi.org/10.1007/978-3-030-31919-9_14

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