Mitigating Both Data Corruption and Content Replay Attacks with Implicit Data Integrity

We study the security of the recently proposed implicit integrity methodology. Implicit integrity is a novel methodology that supports corruption detection without producing, storing or verifying mathematical summaries of the content such as MACs or ICVs, as typically done today. The main idea behind implicit integrity is that, whereas typical user data demonstrate patterns such as repeated bytes or words, decrypted data resulting from corrupted ciphertexts no longer demonstrate such patterns. Thus, by checking the entropy of decrypted ciphertexts, corruption can be possibly detected. Past contributions to the implicit integrity methodology have focused on observed patterns on client and server data that motivate the methodology, entropy definitions for arbitrarily small messages, and constructions that mitigate data corruption attacks. In this paper, we extend the known analytical results concerning implicit integrity addressing content replay attacks as well. We demonstrate that the class of cryptographic constructions known as `random oracles according to observer functions', which has been proposed for mitigating data corruption attacks, is actually simultaneously secure under two different adversary models: an input perturbing adversary performing content corruption attacks, and an oracle replacing adversary performing content replay attacks.