10 citations · 23 across the 8 of their papers we have counts for
10 papers
Inner Momentum for Differentially Private Muon
Bishnu Bhusal, Minh Vu, Ben Southworth +3
Differentially private training clips each per-example gradient before adding noise. This clipping is radial for each example, yet unequal clipping factors can distort the relative…
Approximate Algorithms for Verifying Differential Privacy with Gaussian Distributions
Bishnu Bhusal, Rohit Chadha, A. Prasad Sistla +1
The verification of differential privacy algorithms that employ Gaussian distributions is little understood. This paper tackles the challenge of verifying such programs by introduc…
Deciding Differential Privacy of Online Algorithms with Multiple Variables
Rohit Chadha, A. Prasad Sistla, Mahesh Viswanathan +1
We consider the problem of checking the differential privacy of online randomized algorithms that process a stream of inputs and produce outputs corresponding to each input. This p…
Stack-Aware Hyperproperties
Ali Bajwa, Minjian Zhang, Rohit Chadha +1
A hyperproperty relates executions of a program and is used to formalize security objectives such as confidentiality, non-interference, privacy, and anonymity. Formally, a hyperpro…
On Linear Time Decidability of Differential Privacy for Programs with Unbounded Inputs
Rohit Chadha, A. Prasad Sistla, Mahesh Viswanathan
We introduce an automata model for describing interesting classes of differential privacy mechanisms/algorithms that include known mechanisms from the literature. These automata ca…
Deciding Accuracy of Differential Privacy Schemes
Gilles Barthe, Rohit Chadha, Paul Krogmeier +2
Differential privacy is a mathematical framework for developing statistical computations with provable guarantees of privacy and accuracy. In contrast to the privacy component of d…