207 research outputs found
Using quantum key distribution for cryptographic purposes: a survey
The appealing feature of quantum key distribution (QKD), from a cryptographic
viewpoint, is the ability to prove the information-theoretic security (ITS) of
the established keys. As a key establishment primitive, QKD however does not
provide a standalone security service in its own: the secret keys established
by QKD are in general then used by a subsequent cryptographic applications for
which the requirements, the context of use and the security properties can
vary. It is therefore important, in the perspective of integrating QKD in
security infrastructures, to analyze how QKD can be combined with other
cryptographic primitives. The purpose of this survey article, which is mostly
centered on European research results, is to contribute to such an analysis. We
first review and compare the properties of the existing key establishment
techniques, QKD being one of them. We then study more specifically two generic
scenarios related to the practical use of QKD in cryptographic infrastructures:
1) using QKD as a key renewal technique for a symmetric cipher over a
point-to-point link; 2) using QKD in a network containing many users with the
objective of offering any-to-any key establishment service. We discuss the
constraints as well as the potential interest of using QKD in these contexts.
We finally give an overview of challenges relative to the development of QKD
technology that also constitute potential avenues for cryptographic research.Comment: Revised version of the SECOQC White Paper. Published in the special
issue on QKD of TCS, Theoretical Computer Science (2014), pp. 62-8
Effects of Architecture on Information Leakage of a Hardware Advanced Encryption Standard Implementation
Side-channel analysis (SCA) is a threat to many modern cryptosystems. Many countermeasures exist, but are costly to implement and still do not provide complete protection against SCA. A plausible alternative is to design the cryptosystem using architectures that are known to leak little information about the cryptosystem\u27s operations. This research uses several common primitive architectures for the Advanced Encryption Standard (AES) and assesses the susceptibility of the full AES system to side-channel attack for various primitive configurations. A combined encryption/decryption core is also evaluated to determine if variation of high-level architectures affects leakage characteristics. These different configurations are evaluated under multiple measurement types and leakage models. The results show that different hardware configurations do impact the amount of information leaked by a device, but none of the tested configurations are able to prevent exploitation
A dynamical systems approach to the discrimination of the modes of operation of cryptographic systems
Evidence of signatures associated with cryptographic modes of operation is
established. Motivated by some analogies between cryptographic and dynamical
systems, in particular with chaos theory, we propose an algorithm based on
Lyapunov exponents of discrete dynamical systems to estimate the divergence
among ciphertexts as the encryption algorithm is applied iteratively. The
results allow to distinguish among six modes of operation, namely ECB, CBC,
OFB, CFB, CTR and PCBC using DES, IDEA, TEA and XTEA block ciphers of 64 bits,
as well as AES, RC6, Twofish, Seed, Serpent and Camellia block ciphers of 128
bits. Furthermore, the proposed methodology enables a classification of modes
of operation of cryptographic systems according to their strength.Comment: 14 pages, 10 figure
Memorization for Good: Encryption with Autoregressive Language Models
Over-parameterized neural language models (LMs) can memorize and recite long
sequences of training data. While such memorization is normally associated with
undesired properties such as overfitting and information leaking, our work
casts memorization as an unexplored capability of LMs. We propose the first
symmetric encryption algorithm with autoregressive language models (SELM). We
show that autoregressive LMs can encode arbitrary data into a compact
real-valued vector (i.e., encryption) and then losslessly decode the vector to
the original message (i.e., decryption) via random subspace optimization and
greedy decoding. While SELM is not amenable to conventional cryptanalysis, we
investigate its security through a novel empirical variant of the classic
IND-CPA (indistinguishability under chosen-plaintext attack) game and show
promising results on security. Our code and datasets are available at
https://github.com/OSU-NLP-Group/SELM.Comment: Main text: 9 pages, 4 figures, 1 table. Work-in-progress. Project
website at https://samuelstevens.me/research/encryption
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