The Multivariate Hidden Number Problem

This work extends the line of research on the hidden number problem. Motivated by studying bit security in finite fields, we define the multivariate hidden number problem. Here, the secret and the multiplier are vectors, and partial information about their dot product is given. Using tools from discrete Fourier analysis introduced by Akavia, Goldwasser and Safra, we show that if one can find the significant Fourier coefficients of some function, then one can solve the multivariate hidden number problem for that function. This allows us to generalise the work of Akavia on the hidden number problem with (non-adaptive) chosen multipliers to all finite fields. We give two further applications of our results, both of which generalise previous works to all (finite) extension fields. The first considers the general (random samples) hidden number problem in F_{p^m} and assumes an advice is given to the algorithm. The second considers a model that allows changing representations, where we show hardness of individual bits for elliptic curve and pairing based functions for elliptic curves over extension fields, as well as hardness of any bit of any component of the Diffie-Hellman secret in F_{p^m} (m>1)

Finding Significant Fourier Coefficients: Clarifications, Simplifications, Applications and Limitations

Ideas from Fourier analysis have been used in cryptography for the last three decades. Akavia, Goldwasser and Safra unified some of these ideas to give a complete algorithm that finds significant Fourier coefficients of functions on any finite abelian group. Their algorithm stimulated a lot of interest in the cryptography community, especially in the context of `bit security'. This manuscript attempts to be a friendly and comprehensive guide to the tools and results in this field. The intended readership is cryptographers who have heard about these tools and seek an understanding of their mechanics and their usefulness and limitations. A compact overview of the algorithm is presented with emphasis on the ideas behind it. We show how these ideas can be extended to a `modulus-switching' variant of the algorithm. We survey some applications of this algorithm, and explain that several results should be taken in the right context. In particular, we point out that some of the most important bit security problems are still open. Our original contributions include: a discussion of the limitations on the usefulness of these tools; an answer to an open question about the modular inversion hidden number problem

Line Integral Solution of Hamiltonian Systems with Holonomic Constraints

In this paper, we propose a second-order energy-conserving approximation procedure for Hamiltonian systems with holonomic constraints. The derivation of the procedure relies on the use of the so-called line integral framework. We provide numerical experiments to illustrate theoretical findings.Comment: 30 pages, 3 figures, 4 table

A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization

This paper presents a fully asynchronous and distributed approach for tackling optimization problems in which both the objective function and the constraints may be nonconvex. In the considered network setting each node is active upon triggering of a local timer and has access only to a portion of the objective function and to a subset of the constraints. In the proposed technique, based on the method of multipliers, each node performs, when it wakes up, either a descent step on a local augmented Lagrangian or an ascent step on the local multiplier vector. Nodes realize when to switch from the descent step to the ascent one through an asynchronous distributed logic-AND, which detects when all the nodes have reached a predefined tolerance in the minimization of the augmented Lagrangian. It is shown that the resulting distributed algorithm is equivalent to a block coordinate descent for the minimization of the global augmented Lagrangian. This allows one to extend the properties of the centralized method of multipliers to the considered distributed framework. Two application examples are presented to validate the proposed approach: a distributed source localization problem and the parameter estimation of a neural network.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0648