Optimal Bounded-Collusion Secure Functional Encryption
Name
2019-314.pdf
Description
Accepted version
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966.53 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ananth, Prabhanjan
Vaikuntanathan, Vinod
Date Issued
November 2019
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
Ananth, Prabhanjan and Vinod Vaikuntanathan. "Optimal Bounded-Collusion Secure Functional Encryption."
TCC: Theory of Cryptography Conference, Lecture Notes in Computer Science, 11891, Springer International Publishing, 2019, 174-198. © 2019 International Association for Cryptologic Research.
Version
Author's final manuscript
Abstract
We construct private-key and public-key functional encryption schemes in the bounded-key setting; that is, secure against adversaries that obtain an a-priori bounded number of functional keys (also known as the collusion bound). An important metric considered in the literature on bounded-key functional encryption schemes is the dependence of the running time of the encryption algorithm on the collusion bound Q = Q(λ) (where λ is the security parameter). It is known that bounded-key functional encryption schemes with encryption complexity growing with ε > 0, for any constant Q1-λ, implies indistinguishability obfuscation. On the other hand, in the public-key setting, it was previously unknown whether we could achieve encryption complexity growing linear with Q, also known as optimal bounded-key FE, based on well-studied assumptions. In this work, we give the first construction of an optimal bounded-key public-key functional encryption scheme under the minimal assumption of the existence of any public-key encryption scheme. Moreover, our scheme supports the class of all polynomial-size circuits. Our techniques also extend to the private-key setting. We achieve a construction of an optimal bounded-key functional encryption in the private-key setting based on the minimal assumption of one-way functions, instead of learning with errors as achieved in prior works.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-030-36030-6_8
