Thomas Johansson
Lund University
259 Papers
2.4K Citations
Thomas Johansson is an academic researcher from Lund University. The author has contributed to research in topics: Stream cipher & Cryptography. The author has an hindex of 50, co-authored 235 publications. Previous affiliations of Thomas Johansson include GM Powertrain Torino.
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Papers
On families of hash functions via geometric codes and concatenation
Jürgen Bierbrauer,Thomas Johansson,Gregory Kabatianskii,Ben Smeets +3 more
- 22 Aug 1993
TL;DR: This paper uses coding theory to give simple explanations of some recent results on universal hashing, and shows how codes derived from Artin-Schreier curves, Hermitian curves and Suzuki curves yield good classes of universal hash functions.
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Progress in Cryptology - INDOCRYPT 2003
TL;DR: A survey of recent work on the linear complexity, thelinear complexity profile, and the k-error linear complexity of sequences and on the joint linear complex of multisequences and a new enumeration theorem on multiseaquences are presented.
SNOW - A new stream cipher
Patrik Ekdahl,Thomas Johansson +1 more
- 01 Jan 2000
TL;DR: A new word-oriented stream cipher, called SNOW, is proposed, consisting of a linear feedback shift register, feeding a nite state machine, and the fastest C implementation requires under 1 clock cycle per running key bit.
144
Renewable Fuels and Electricity for a Growing World Economy: Defining and Achieving the Potential
TL;DR: The Energy Technology Options Conference opened with a presentation from Professor Thomas Johansson on the prospects for renewable energy in a global context as discussed by the authors, based upon a study commissioned by the United Nations Solar Energy Group for Environment and Development, which he chairs.
Fast Correlation Attacks through Reconstruction of Linear Polynomials
Thomas Johansson,Fredrik U. Jönsson +1 more
- 20 Aug 2000
TL;DR: In this paper, a fast correlation attack is proposed to restore the initial content of a linear feedback shift register in a stream cipher using a detected correlation with the output sequence, by modeling this problem as the problem of learning a binary linear multivariate polynomial.
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