Make Quantum Indistinguishability Great Again

03/01/2020
by   Tommaso Gagliardoni, et al.
0

In this work we study the (superposition-based, or QS2) quantum security of public key encryption schemes, originally initiated by Boneh and Zhandry (CRYPTO 2013, for a classical challenge indistinguishability phase) and improved by Gagliardoni et al. (CRYPTO 2016, for the symmetric key case). For public key encryption schemes, no notion of quantum security with a quantum indistinguishability phase exists. In this work we close this gap by using so-called type-2 operators for encrypting the challenge message. This brings non-trivial obstacles: On the one hand, public key encryption schemes typically cannot recover the randomness during decryption. On the other hand, many real-world schemes suffer from a small probability of decryption failure. Nevertheless, we identify a class of encryption schemes, which we call recoverable, that allow to avoid decryption failures given knowledge of the original encryption randomness, and we show that for these schemes the type-2 operator can be efficiently implemented even without knowledge of the secret key. This means that, for the public key case, type-2 operators are actually very natural. We also observe that many real-world quantum-resistant schemes, including many NIST candidates, are of this type. Equipped with these results, we (1) give the first quantum security notion (qINDqCPA) for public key encryption with a quantum indistinguishability phase, (2) prove that the canonical LWE-based encryption scheme achieves our security notion, (3) show that our notion is strictly stronger than existing security notions, (4) study the general classification of quantum-resistant public key encryption schemes, and (5) compare our results to a concurrent and independent work by Chevalier et al. (2020).

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro