Quantum-safe cryptography: crossroads of coding theory and cryptography

We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.

Width, Largeness and Index Theory

In this note, we review some recent developments related to metric aspects of scalar curvature from the point of view of index theory for Dirac operators. In particular, we revisit index-theoretic approaches to a conjecture of Gromov on the width of Riemannian bands M×[−1,1], and on a conjecture of Rosenberg and Stolz on the non-existence of complete positive scalar curvature metrics on M×R. We show that there is a more general geometric statement underlying both of them implying a quantitative negative upper bound on the infimum of the scalar curvature of a complete metric on M×R if the scalar curvature is positive in some neighborhood. We study (A hat-)iso-enlargeable spin manifolds and related notions of width for Riemannian manifolds from an index-theoretic point of view. Finally, we list some open problems arising in the interplay between index theory, largeness properties and width.

Consolidating Security Notions in Hardware Masking

In this paper, we revisit the security conditions of masked hardware implementations. We describe a new, succinct, information-theoretic condition called d-glitch immunity which is both necessary and sufficient for security in the presence of glitches. We show that this single condition includes, but is not limited to, previous security notions such as those used in higher-order threshold implementations and in abstractions using ideal gates. As opposed to these previously known necessary conditions, our new condition is also sufficient. On the other hand, it excludes avoidable notions such as uniformity. We also treat the notion of (strong) noninterference from an information-theoretic point-of-view in order to unify the different security concepts and pave the way to the verification of composability in the presence of glitches. We conclude the paper by demonstrating how the condition can be used as an efficient and highly generic flaw detection mechanism for a variety of functions and schemes based on different operations.

“Statistics 103” for Multitarget Tracking

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.

Revisiting and Improving Algorithms for the 3XOR Problem

The 3SUM problem is a well-known problem in computer science and many geometric problems have been reduced to it. We study the 3XOR variant which is more cryptologically relevant. In this problem, the attacker is given black-box access to three random functions F,G and H and she has to find three inputs x, y and z such that F(x) ⊕ G(y) ⊕ H(z) = 0. The 3XOR problem is a difficult case of the more-general k-list birthday problem. Wagner’s celebrated k-list birthday algorithm, and the ones inspired by it, work by querying the functions more than strictly necessary from an information-theoretic point of view. This gives some leeway to target a solution of a specific form, at the expense of processing a huge amount of data. However, to handle such a huge amount of data can be very difficult in practice. This is why we first restricted our attention to solving the 3XOR problem for which the total number of queries to F, G and H is minimal. If they are n-bit random functions, it is possible to solve the problem with roughly

Control and the Analysis of Cancer Growth Models

In this note, we analyze two cancer dynamical models from a system-theoretic point of view. The first model is based upon stochastic controlled versions of the classical Lotka-Volterra equations. Here we consider from a controls point of view the utility of employing ultrahigh dose flashes in radiotherapy. The second is based on work of Norton-Simon-Massagué growth model that takes into account the heterogeneity of a tumor cell population. We indicate an optimal strategy based on linear quadratic control applied to a linear transformed model.

Learning algorithm analysis for deep neural network with ReLu activation functions

In the article, emphasis is put on the modern artificial neural network structure, which in the literature is known as a deep neural network. Network includes more than one hidden layer and comprises many standard modules with ReLu nonlinear activation function. A learning algorithm includes two standard steps, forward and backward, and its effectiveness depends on the way the learning error is transported back through all the layers to the first layer. Taking into account all the dimensionalities of matrixes and the nonlinear characteristics of ReLu activation function, the problem is very difficult from a theoretic point of view. To implement simple assumptions in the analysis, formal formulas are used to describe relations between the structure of every layer and the internal input vector. In practice tasks, neural networks’ internal layer matrixes with ReLu activations function, include a lot of null value of weight coefficients. This phenomenon has a negatives impact on the effectiveness of the learning algorithm convergences. A theoretical analysis could help to build more effective algorithms.