10 papers
Forecast and Model Predictive Control of Distributed Energy Resource Aggregators for Net-Demand Balancing
Obai Bahwal, Oliver Kosut, Lalitha Sankar +1
With the rapid demand for energy, even the incorporation of bulk renewable energy sources is not entirely sufficient to meet demand besides adding supply uncertainty. Distributed E…
DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum
Naima Tasnim, Lalitha Sankar, Oliver Kosut
Differentially private stochastic gradient descent (DP-SGD) has become the standard framework for privacy-preserving machine learning, yet its reliance on a fixed gradient clipping…
ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport
Atefeh Gilani, Sajani Vithana, Carol Xuan Long +3
Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into generated tokens that either flags LLM…
Information-Theoretic Privacy with General Distortion Constraints
Kousha Kalantari, Oliver Kosut, Lalitha Sankar
The privacy-utility tradeoff problem is formulated as determining the privacy mechanism (random mapping) that minimizes the mutual information (a metric for privacy leakage) betwee…
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…
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…