Ingenico (France)

Abstract The world is filled with hard and complex problems, oftentimes requiring involved solutions. In large organizations attempting to solve these types of problems, a mindset shift and key candidate methodologies centered on collaborative systems thinking culture (CSTC) can assist significantly. The paper explores the state of the practice, change involved with implementing systems thinking, impacts of a collaborative approach within an organization, as well as the seven phases that a reader can introduce into their organization to realize some of the benefits. The same approach was used to create this paper under collective authorship from Cohort 6 of the INCOSE Technical Leadership Institute (TLI); an international group of individuals collaborating exclusively through virtual platforms. From writing papers to executing large technical programs, the CSTC approach will prepare technical teams for tackling challenging problems in an inclusive way with the intent to finish projects on time while also cultivating healthy systems engineering habits and practices. This lessens the reliance on corporate engineering procedures to drive collaborative behavior by fiat. Finally, blending CSTC into the fabric and culture of an organization is emphasized as being needed for the full benefit. That benefit includes saving programs by moving to a CSTC.

This chapter explores ways of performing a kind of commutative task by N parties, of which a particular scenario of contract signing is a canonical example. Tasks are defined as commutative if the order in which parties perform them can be freely changed without affecting the final result. It is easy to see that arbitrary N-party commutative tasks cannot be completed in less than N − 1 basic time units.

In this paper we study six 2nd round SHA-3 candidates from a side-channel cryptanalysis point of view. For each of them, we give the exact procedure and appropriate choice of selection functions to perform the attack. Depending on their inherent structure and the internal primitives used (Sbox, addition or XOR), some schemes are more prone to side channel analysis than others, as shown by our simulations.

CubeHash is a family of hash functions submitted by Bernstein as a SHA-3 candidate. In this paper, we provide two different cryptanalysis approaches concerning its collision resistance. Thanks to the first approach, related to truncated differentials, we computed a collision for the CubeHash -1/36 hash function, i.e. when for each iteration 36 bytes of message are incorporated and one call to the permutation is applied. Then, the second approach, already used by Dai, much more efficient and based on a linearization of the scheme, allowed us to compute a collision for the CubeHash -2/4 hash function. Finally, a theoretical collision attack against CubeHash -2/3, CubeHash -4/4 and CubeHash -4/3 is described. This is currently by far the best known cryptanalysis result on this SHA-3 candidate.

Bernstein’s CubeHash is a hash function family that includes four functions submitted to the NIST Hash Competition. A CubeHash function is parametrized by a number of rounds r, a block byte size b, and a digest bit length h (the compression function makes r rounds, while the finalization function makes 10r rounds). The 1024-bit internal state of CubeHash is represented as a five-dimensional hypercube. The submissions to NIST recommends r = 8, b = 1, and h ∈ {224,256,384,512}.This paper presents the first external analysis of CubeHash, with improved standard generic attacks for collisions and preimages a multicollision attack that exploits fixed points a study of the round function symmetries a preimage attack that exploits these symmetries a practical collision attack on a weakened version of CubeHash a study of fixed points and an example of nontrivial fixed point high-probability truncated differentials over 10 rounds Since the first publication of these results, several collision attacks for reduced versions of CubeHash were published by Dai, Peyrin, et al. Our results are more general, since they apply to any choice of the parameters, and show intrinsic properties of the CubeHash design, rather than attacks on specific versions.KeywordsSymmetric StateSymmetry ClassCompression FunctionMessage BlockCollision AttackThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.