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https://doi.org/10.29007/fv2n...
Article . 2019 . Peer-reviewed
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Article . 2019
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Detecting Faults in Inner-Product Masking Scheme - IPM-FD: IPM with Fault Detection

Authors: Cheng, Wei; Carlet, Claude; Goli, Kouassi; Danger, Jean-Luc; Guilley, Sylvain;

Detecting Faults in Inner-Product Masking Scheme - IPM-FD: IPM with Fault Detection

Abstract

Side-channel analysis and fault injection attacks are two typical threats to cryptographic implementations, especially in modern embedded devices. Thus there is an insistent demand for dual side-channel and fault injection protections. As it is known, masking scheme is a kind of provable countermeasures against side-channel attacks. Recently, inner product masking (IPM) was proposed as a promising higher-order masking scheme against side-channel analysis, but not for fault injection attacks. In this paper, we devise a new masking scheme named IPM-FD. It is built on IPM, which enables fault detection. This novel masking scheme has three properties: the security orders in the word-level probing model, bit-level probing model, and the number of detected faults. IPM-FD is proven secure both in the word-level and in the bit-level probing models, and allows for end-to-end fault detection against fault injection attacks.Furthermore, we illustrate its security order by linking it to one defining parameters of linear code, and show its implementation cost by applying IPM-FD to AES-128.

Country
France
Keywords

[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Inner product masking, Side-Channel Analysis, Fault Detection, Fault-Injection Attacks, 620, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]

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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!
1
Average
Average
Average
Green