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Deniable Fully Homomorphic Encryption from Learning With Errors
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| Conference: | CRYPTO 2021 |
| Abstract: | We define and construct {\it Deniable Fully Homomorphic Encryption} based on the Learning With Errors (LWE) polynomial hardness assumption. Deniable FHE enables storing encrypted data in the cloud to be processed securely without decryption, maintaining deniability of the encrypted data, as well the prevention of vote-buying in electronic voting schemes where encrypted votes can be tallied without decryption. Our constructions achieve {\it compactness} independently of the level of deniability- both the size of the public key and the size of the ciphertexts are bounded by a fixed polynomial, independent of the faking probability achieved by the scheme. This is in contrast to all previous constructions of deniable encryption schemes (even without requiring homomorphisms) which are based on polynomial hardness assumptions, originating with the seminal work of Canetti, Dwork, Naor and Ostrovsky (CRYPTO 1997) in which the ciphertext size grows with the inverse of the faking probability. Canetti {\it et al.} argued that this dependence ``seems inherent'', but our constructions illustrate this is not the case. We note that the Sahai-Waters (STOC13) construction of deniable encryption from indistinguishability-obfuscation achieves compactness and can be easily modified to achieve deniable FHE as well, but it requires multiple, stronger sub-exponential hardness assumptions, which are furthermore not post-quantum secure. In contrast, our constructions rely only on the LWE polynomial hardness assumption, as currently required for FHE even without deniability. The running time of our encryption algorithm depends on the inverse of the faking probability, thus the scheme falls short of achieving simultaneously compactness, negligible deniability probability {\it and} polynomial encryption time. Yet, we believe that achieving compactness is a fundamental step on the way to achieving all properties simultaneously as has been the historical journey for other primitives such as functional encryption. Interestingly, we note that our constructions support large message spaces, whereas previous constructions were bit by bit, and can be run in online-offline model of encryption, where the bulk of computation is independent of the message and may be performed in an offline pre-processing phase. The running time of the online phase, is independent of the faking probability, whereas the offline encryption run-time grows with the inverse of the faking probability. At the heart of our constructions is a new way to use bootstrapping to obliviously generate FHE ciphertexts so that it supports faking under coercion. |
Video from CRYPTO 2021
BibTeX
@inproceedings{crypto-2021-31102,
title={Deniable Fully Homomorphic Encryption from Learning With Errors},
publisher={Springer-Verlag},
doi={10.1007/978-3-030-84245-1_22},
author={Shweta Agrawal and Shafi Goldwasser and Saleet Mossel},
year=2021
}