5 papers
A Nonlinear Deficiency Identity for the Riemann Zeta Function with Optimal Approximation Rates
Meisam Mohammady
We introduce a deficiency-based representation and approximation framework for values of the Riemann zeta function. The method is based on comparing two nonlinear accumulation mech…
Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
Meisam Mohammady, Qin Yang, Nicholas Stout +4
Differentially Private Stochastic Gradient Descent (DP-SGD) is a cornerstone technique for ensuring privacy in deep learning, widely used in both training from scratch and fine-tun…
PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
Qin Yang, Nicholas Stout, Meisam Mohammady +6
Differentially Private Stochastic Gradient Descent (DP-SGD) is a standard method for enforcing privacy in deep learning, typically using the Gaussian mechanism to perturb gradient…
RDP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Meisam Mohammady, Shangyu Xie, Yuan Hong +4
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly…
Preserving Both Privacy and Utility in Network Trace Anonymization
Meisam Mohammady, Lingyu Wang, Yuan Hong +3
As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctan…