9 citations · 38 across the 15 of their papers we have counts for
4 papers · 1 filter
Research Challenges in Designing Differentially Private Text Generation Mechanisms
Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu +1
Accurately learning from user data while ensuring quantifiable privacy guarantees provides an opportunity to build better Machine Learning (ML) models while maintaining user trust.…
A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan +1
Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy-pr…
Differentially Private Adversarial Robustness Through Randomized Perturbations
Nan Xu, Oluwaseyi Feyisetan, Abhinav Aggarwal +2
Deep Neural Networks, despite their great success in diverse domains, are provably sensitive to small perturbations on correctly classified examples and lead to erroneous predictio…
On Primes, Log-Loss Scores and (No) Privacy
Abhinav Aggarwal, Zekun Xu, Oluwaseyi Feyisetan +1
Membership Inference Attacks exploit the vulnerabilities of exposing models trained on customer data to queries by an adversary. In a recently proposed implementation of an auditin…