Quantum algorithms for algebraic problems

Quantum computers can execute algorithms that dramatically outperform classical computation. As the best-known example, Shor discovered an efficient quantum algorithm for factoring integers, whereas factoring appears to be difficult for classical computers. Understanding what other computational problems can be solved significantly faster using quantum algorithms is one of the major challenges in the theory of quantum computation, and such algorithms motivate the formidable task of building a large-scale quantum computer. This article reviews the current state of quantum algorithms, focusing on algorithms with superpolynomial speedup over classical computation, and in particular, on problems with an algebraic flavor.Comment: 52 pages, 3 figures, to appear in Reviews of Modern Physic

Study of information transfer optimization for communication satellites

The results are presented of a study of source coding, modulation/channel coding, and systems techniques for application to teleconferencing over high data rate digital communication satellite links. Simultaneous transmission of video, voice, data, and/or graphics is possible in various teleconferencing modes and one-way, two-way, and broadcast modes are considered. A satellite channel model including filters, limiter, a TWT, detectors, and an optimized equalizer is treated in detail. A complete analysis is presented for one set of system assumptions which exclude nonlinear gain and phase distortion in the TWT. Modulation, demodulation, and channel coding are considered, based on an additive white Gaussian noise channel model which is an idealization of an equalized channel. Source coding with emphasis on video data compression is reviewed, and the experimental facility utilized to test promising techniques is fully described

Information Geometry

This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience

Security, Scalability and Privacy in Applied Cryptography

In the modern digital world, cryptography finds its place in countless applications. However, as we increasingly use technology to perform potentially sensitive tasks, our actions and private data attract, more than ever, the interest of ill-intentioned actors. Due to the possible privacy implications of cryptographic flaws, new primitives’ designs need to undergo rigorous security analysis and extensive cryptanalysis to foster confidence in their adoption. At the same time, implementations of cryptographic protocols should scale on a global level and be efficiently deployable on users’ most common devices to widen the range of their applications. This dissertation will address the security, scalability and privacy of cryptosystems by presenting new designs and cryptanalytic results regarding blockchain cryptographic primitives and public-key schemes based on elliptic curves. In Part I, I will present the works I have done in regards to accumulator schemes. More precisely, in Chapter 2, I cryptanalyze Au et al. Dynamic Universal Accumulator, by showing some attacks which can completely take over the authority who manages the accumulator. In Chapter 3, I propose a design for an efficient and secure accumulator-based authentication mechanism, which is scalable, privacy-friendly, lightweight on the users’ side, and suitable to be implemented on the blockchain. In Part II, I will report some cryptanalytical results on primitives employed or considered for adoption in top blockchain-based cryptocurrencies. In particular, in Chapter 4, I describe how the zero-knowledge proof system and the commitment scheme adopted by the privacy-friendly cryptocurrency Zcash, contain multiple subliminal channels which can be exploited to embed several bytes of tagging information in users’ private transactions. In Chapter 5, instead, I report the cryptanalysis of the Legendre PRF, employed in a new consensus mechanism considered for adoption by the blockchain-based platform Ethereum, and attacks for further generalizations of this pseudo-random function, such as the Higher-Degree Legendre PRF, the Jacobi Symbol PRF, and the Power-Residue PRF. Lastly, in Part III, I present my line of research on public-key primitives based on elliptic curves. In Chapter 6, I will describe a backdooring procedure for primes so that whenever they appear as divisors of a large integer, the latter can be efficiently factored. This technique, based on elliptic curves Complex Multiplication theory, enables to eventually generate non-vulnerable certifiable semiprimes with unknown factorization in a multi-party computation setting, with no need to run a statistical semiprimality test common to other protocols. In Chapter 7, instead, I will report some attack optimizations and specific implementation design choices that allow breaking a reduced-parameters instance, proposed by Microsoft, of SIKE, a post-quantum key-encapsulation mechanism based on isogenies between supersingular elliptic curves

Glosarium Matematika

273 p.; 24 cm

Non-Convex and Geometric Methods for Tomography and Label Learning

Data labeling is a fundamental problem of mathematical data analysis in which each data point is assigned exactly one single label (prototype) from a finite predefined set. In this thesis we study two challenging extensions, where either the input data cannot be observed directly or prototypes are not available beforehand. The main application of the first setting is discrete tomography. We propose several non-convex variational as well as smooth geometric approaches to joint image label assignment and reconstruction from indirect measurements with known prototypes. In particular, we consider spatial regularization of assignments, based on the KL-divergence, which takes into account the smooth geometry of discrete probability distributions endowed with the Fisher-Rao (information) metric, i.e. the assignment manifold. Finally, the geometric point of view leads to a smooth flow evolving on a Riemannian submanifold including the tomographic projection constraints directly into the geometry of assignments. Furthermore we investigate corresponding implicit numerical schemes which amount to solving a sequence of convex problems. Likewise, for the second setting, when the prototypes are absent, we introduce and study a smooth dynamical system for unsupervised data labeling which evolves by geometric integration on the assignment manifold. Rigorously abstracting from ``data-label'' to ``data-data'' decisions leads to interpretable low-rank data representations, which themselves are parameterized by label assignments. The resulting self-assignment flow simultaneously performs learning of latent prototypes in the very same framework while they are used for inference. Moreover, a single parameter, the scale of regularization in terms of spatial context, drives the entire process. By smooth geodesic interpolation between different normalizations of self-assignment matrices on the positive definite matrix manifold, a one-parameter family of self-assignment flows is defined. Accordingly, the proposed approach can be characterized from different viewpoints such as discrete optimal transport, normalized spectral cuts and combinatorial optimization by completely positive factorizations, each with additional built-in spatial regularization