Anton Tkachenko

dblp:248/8022 · DBLP profile ↗
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4ranked-venue papers
0as first author
2since 2021 · last 2022
0009-0005-1068-7971ORCID · corroborated

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Theory of computation · 2 · 1 since 2021Artificial intelligence and machine learning · 1Security and privacy · 1 · 1 since 2021
YearPublicationVenuePosition
2022 C-differential bent functions and perfect nonlinearity
Pantelimon Stanica, Sugata Gangopadhyay, Aaron Geary, Constanza Riera, Anton Tkachenko
Discret. Appl. Math.5
2021 An Extension of the Avalanche Criterion in the Context of c-Differentials
abstract
The Strict Avalanche Criterion (SAC) is a property of vectorial Boolean functions that is used in the construction of strong S-boxes. We show in this paper how to generalize the concept of SAC to address possible c-differential attacks, in the realm of finite fields. We define the concepts of c-Strict Avalanche Criterion (c-SAC) and c-Strict Avalanche Criterion of order m (c-SAC(m)), and generalize results of (Li and Cusick, 2005). We also show computationally how the new definition is not equivalent to the existing concepts of c-bent1-ness (Stanica et al., 2020), nor (for n = m) PcN-ness (Ellingsen et al., 2020)
Pål Ellingsen, Constanza Riera, Pantelimon Stanica, Anton Tkachenko
SECRYPT4
2020 The Concept of Developing a Decision Support System for the Epidemic Morbidity Control
Sergiy Yakovlev, Kseniia Bazilevych, Dmytro Chumachenko, Tetyana Chumachenko, Leonid Hulianytskyi, Ievgen Meniailov, Anton Tkachenko
IDDM7
2020 C-Differentials, Multiplicative Uniformity, and (Almost) Perfect c-Nonlinearity
abstract
In this paper we define a new (output) multiplicative differential, and the corresponding c-differential uniformity. With this new concept, even for characteristic 2, there are perfect c-nonlinear (PcN) functions. We first characterize the c-differential uniformity of a function in terms of its Walsh transform. We further look at some of the known perfect nonlinear (PN) functions and show that only one remains a PcN function, under a different condition on the parameters. In fact, the p-ary Gold PN function increases its c-differential uniformity significantly, under some conditions on the parameters. We then precisely characterize the c-differential uniformity of the inverse function (in any dimension and characteristic), relevant for the Rijndael (and Advanced Encryption Standard) block cipher.
Pål Ellingsen, Patrick Felke, Constanza Riera, Pantelimon Stanica, Anton Tkachenko
IEEE Trans. Inf. Theory5