4 accepted papers
Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. To quench this thirst, we present Privasis (i.e., privacy oasis), the first million-scale fully synthetic dataset entirely built fr…
This paper studies a range of AI/ML trust concepts, including memorization, data poisoning, and copyright, which can be modeled as constraints on the influence of data on a (trained) model, characterized by the outcome difference from a processing function (training algorithm). In this realm, we sho…
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, resulting in failure to provide…
We derive a new computational algorithm for Barrett technique for modular polynomial multiplication, termed BA-P. Residue arithmetic is applied to BA-P to obtain a new Barrett algorithm for modular polynomial multiplication (BA-MPM). The work is focused on an algorithm that carries out computation u…