Publication
CYSARM 2020
Conference paper

Efficiency Improvements for Encrypt-to-Self

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Abstract

Recent work by Pijnenburg and Poettering (ESORICS'20) explores the novel cryptographic Encrypt-to-Self primitive that is dedicated to use cases of symmetric encryption where encryptor and decryptor coincide. The primitive is envisioned to be useful whenever a memory-bounded computing device is required to encrypt some data with the aim of temporarily depositing it on an untrusted storage device. While the new primitive protects the confidentiality of payloads as much as classic authenticated encryption primitives would do, it provides considerably better authenticity guarantees: Specifically, while classic solutions would completely fail in a context involving user corruptions, if an encrypt-to-self scheme is used to protect the data, all ciphertexts and messages fully remain unforgeable. To instantiate their encrypt-to-self primitive, Pijnenburg et.al propose a mode of operation of the compression function of a hash function, with a carefully designed encoding function playing the central role in the serialization of the processed message and associated data. In the present work we revisit the design of this encoding function. Without questioning its adequacy for securely accomplishing the encrypt-to-self job, we improve on it from a technical/implementational perspective by proposing modifications that alleviate certain conditions that would inevitably require implementations to disrespect memory alignment restrictions imposed by the word-wise operation of modern CPUs, ultimately leading to performance penalties. Our main contributions are thus to propose an improved encoding function, to explain why it offers better performance, and to prove that it provides as much security as its predecessor. We finally report on our open-source implementation of the encrypt-to-self primitive based on the new encoding function. For the full version of this article, see https://arxiv.org/abs/2009.02667 arXiv:2009.02667.

Date

13 Nov 2020

Publication

CYSARM 2020

Authors

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