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DEFAULT: Cipher Level Resistance Against Differential Fault Attack

Authors: Anubhab Baksi; Shivam Bhasin; Jakub Breier; Mustafa Khairallah; Thomas Peyrin; Sumanta Sarkar; Siang Meng Sim;

DEFAULT: Cipher Level Resistance Against Differential Fault Attack

Abstract

Differential Fault Analysis (DFA) is a well known cryptanalytic tech- nique that exploits faulty outputs of an encryption device. Despite its popularity and similarity with the classical Differential Analysis (DA), a thorough analysis explaining DFA from a designer’s point-of-view is missing in the literature. To the best of our knowledge, no DFA immune block cipher at an algorithmic level has been proposed so far. Furthermore, all known DFA countermeasures somehow depend on the device/protocol or on the implementation such as duplication/comparison. As all of these are outside the scope of the cipher designer, we focus on designing a primitive which can protect from DFA on its own. We present the first concept of cipher level DFA resistance which does not rely on any device/protocol related assumption, nor does it depend on any form of duplication. Our construction is simple, software/hardware friendly and DFA security scales up with the state size. It can be plugged before and/or after (almost) any symmetric key cipher and will ensure a non-trivial search complexity against DFA. One key component in our DFA protection layer is an SBox with linear structures. Such SBoxes have never been used in cipher design as they generally perform poorly against differential attacks. We argue that they in fact represent an interesting trade-off between good cryptographic properties and DFA resistance. As a proof of concept, we construct a DFA protecting layer, named DEFAULT-LAYER, as well as a full-fledged block cipher DEFAULT. Our solutions compare favorably to the state-of-the-art, offering advantages over the sophisticated duplication based solutions like impeccable circuits/CRAFT or infective countermeasures.

Keywords

Z004, QA76

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Top 10%
Green