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Adaptively Simulation-Secure Attribute-Hiding Predicate Encryption
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| Conference: | ASIACRYPT 2018 |
| Abstract: | This paper demonstrates how to achieve simulation-based strong attribute hiding against adaptive adversaries for predicate encryption (PE) schemes supporting expressive predicate families under standard computational assumptions in bilinear groups. Our main result is a simulation-based adaptively strongly partially-hidingPE (PHPE) scheme for predicates computing arithmetic branching programs (ABP) on public attributes, followed by an inner-product predicate on private attributes. This simultaneously generalizes attribute-based encryption (ABE) for boolean formulas and ABP’s as well as strongly attribute-hiding PE schemes for inner products. The proposed scheme is proven secure for any a priori bounded number of ciphertexts and an unbounded (polynomial) number of decryption keys, which is the best possible in the simulation-based adaptive security framework. This directly implies that our construction also achieves indistinguishability-based strongly partially-hiding security against adversaries requesting an unbounded (polynomial) number of ciphertexts and decryption keys. The security of the proposed scheme is derived under (asymmetric version of) the well-studied decisional linear (DLIN) assumption. Our work resolves an open problem posed by Wee in TCC 2017, where his result was limited to the semi-adaptive setting. Moreover, our result advances the current state of the art in both the fields of simulation-based and indistinguishability-based strongly attribute-hiding PE schemes. Our main technical contribution lies in extending the strong attribute hiding methodology of Okamoto and Takashima [EUROCRYPT 2012, ASIACRYPT 2012] to the framework of simulation-based security and beyond inner products. |
BibTeX
@inproceedings{asiacrypt-2018-29178,
title={Adaptively Simulation-Secure Attribute-Hiding Predicate Encryption},
booktitle={Advances in Cryptology – ASIACRYPT 2018},
series={Lecture Notes in Computer Science},
publisher={Springer},
volume={11273},
pages={640-672},
doi={10.1007/978-3-030-03329-3_22},
author={Pratish Datta and Tatsuaki Okamoto and Katsuyuki Takashima},
year=2018
}