Upslices, Downslices, and Secret-Sharing with Complexity of 1.5n
Benny Applebaum, Oded Nir
Abstract
A secret-sharing scheme allows to distribute a secret among parties such that only some predefined authorized'' sets of parties can reconstruct the secret, and all other unauthorized'' sets learn nothing about .
The collection of authorized/unauthorized sets can be captured by a monotone function .
In this paper, we focus on monotone functions that all their min-terms are sets of size , and on their duals -- monotone functions whose max-terms are of size . We refer to these classes as -upslices and -downslices, and note that these natural families correspond to monotone -regular DNFs and monotone -regular CNFs. We derive the following results.
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(General downslices) Every downslice can be realized with total share size of . Since every monotone function can be cheaply decomposed into downslices, we obtain a similar result for general access structures improving the previously known complexity of Applebaum, Beimel, Nir and Peter (STOC 2020). We also achieve a minor improvement in the exponent of linear secrets sharing schemes.
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(Random mixture of upslices) Following Beimel and Farras (TCC 2020) who studied the complexity of random DNFs with constant-size terms, we consider the following general distribution over monotone DNFs: For each width value , uniformly sample monotone terms of size , where is an arbitrary vector of non-negative integers. We show that, except with exponentially small probability, can be realized with share size of and can be linearly realized with an exponent strictly smaller than . Our proof also provides a candidate distribution for ``exponentially-hard'' access structure.
We use our results to explore connections between several seemingly unrelated questions about the complexity of secret-sharing schemes such as worst-case vs. average-case, linear vs. non-linear and primal vs. dual access structures. We prove that, in at least one of these settings, there is a significant gap in secret-sharing complexity.
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Install the CLIlune papers get e31aeeda-75ad-4a65-908b-dcbf0c2382daCited by top-tier papers4
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- A Sharp Characterization of PessilandShuichi Hirahara, Mikito NanashimaSTOC 2026
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