388 citations · 1.5k across the 94 of their papers we have counts for
5 papers · 1 filter
An information theorist's tour of differential privacy
Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut +1
Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, dif…
Linear Dynamics meets Linear MDPs: Closed-Form Optimal Policies via Reinforcement Learning
Abed AlRahman Al Makdah, Oliver Kosut, Lalitha Sankar +1
Many applications -- including power systems, robotics, and economics -- involve a dynamical system interacting with a stochastic and hard-to-model environment. We adopt a reinforc…
GeoClip: Geometry-Aware Clipping for Differentially Private SGD
Atefeh Gilani, Naima Tasnim, Lalitha Sankar +1
Differentially private stochastic gradient descent (DP-SGD) is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in…
Optimizing Noise Distributions for Differential Privacy
Atefeh Gilani, Juan Felipe Gomez, Shahab Asoodeh +3
We propose a unified optimization framework for designing continuous and discrete noise distributions that ensure differential privacy (DP) by minimizing Rényi DP, a variant of DP,…
Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions
Naima Tasnim, Atefeh Gilani, Lalitha Sankar +1
We introduce a differentially private (DP) algorithm called reveal-or-obscure (ROO) to generate a single representative sample from a dataset of observations drawn i.i.d. from…