arXiv subjects

Elvira Moreno

Publications and source records attributed to Elvira Moreno.

4 recordsLinked to original sources

arXiv2026

Automated Template-free Synthesis of Instruction-Centric Leakage Contracts for Black-Box CPUs

Side-channel attacks pose a significant security threat for modern computing platforms, because they exploit subtle discrepancies in CPU behaviors to leak sensitive information. To model the information leaked by a CPU via microarchitectural side-channels, recent work proposed leakage contracts: an ISA-level security abstraction that provides the foundations for secure CPU programming. Unfortunately, due to the complexity of current microarchitectures, devising a leakage contract for a CPU requires extensive manual effort and thus modern CPUs lack dedicated leakage contracts. We present a methodology to extract instruction-centric leakage contracts for major CPU architectures with minimal manual intervention. We implemented this technique in malcos, the first template-free tool that automates the synthesis of leakage contracts for black-box CPUs. We evaluate malcos on x86 and ARM CPUs, and show that the contracts it synthesizes are precise and sound with respect to all leaks observed during synthesis. Our results demonstrate that learning leakage contracts from black-box CPUs is feasible.

cs.CR↗

arXiv2025

Spectral Methods for Polynomial Optimization

We present a hierarchy of tractable relaxations to obtain lower bounds on the minimum value of a polynomial over a constraint set defined by polynomial equations. In contrast to previous convex relaxation techniques for this problem, our method is based on computing the smallest generalized eigenvalue of a pair of matrices derived from the problem data, which can be accomplished for large problem instances using off-the-shelf software. We characterize the algebraic structure in a problem that facilitates the application of our framework, and we observe that our method is applicable for all polynomial optimization problems with bounded constraint sets. Our construction also yields a nested sequence of structured convex outer approximations of a bounded algebraic variety with the property that linear optimization over each approximation reduces to an eigenvalue computation. Finally, we present numerical experiments on representative problems in which we demonstrate the scalability of our approach compared to convex relaxation methods derived from sums-of-squares certificates of nonnegativity.

math.OC↗

arXiv2023

Kernel quadrature with randomly pivoted Cholesky

This paper presents new quadrature rules for functions in a reproducing kernel Hilbert space using nodes drawn by a sampling algorithm known as randomly pivoted Cholesky. The resulting computational procedure compares favorably to previous kernel quadrature methods, which either achieve low accuracy or require solving a computationally challenging sampling problem. Theoretical and numerical results show that randomly pivoted Cholesky is fast and achieves comparable quadrature error rates to more computationally expensive quadrature schemes based on continuous volume sampling, thinning, and recombination. Randomly pivoted Cholesky is easily adapted to complicated geometries with arbitrary kernels, unlocking new potential for kernel quadrature.

math.NA↗

arXiv2021

On random walks and switched random walks on homogeneous spaces

We prove new mixing rate estimates for the random walks on homogeneous spaces determined by a probability distribution on a finite group $G$. We introduce the switched random walk determined by a finite set of probability distributions on $G$, prove that its long-term behavior is determined by the Fourier joint spectral radius of the distributions and give hermitian sum-of-squares algorithms for the effective estimation of this quantity.

math.PR↗