9 citations · 20 across the 7 of their papers we have counts for
7 papers
Reconstructing Test Labels from Noisy Loss Functions
Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu +2
Machine learning classifiers rely on loss functions for performance evaluation, often on a private (hidden) dataset. In a recent line of research, label inference was introduced as…
Label Inference Attacks from Log-loss Scores
Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu +2
Log-loss (also known as cross-entropy loss) metric is ubiquitously used across machine learning applications to assess the performance of classification algorithms. In this paper,…
On a Utilitarian Approach to Privacy Preserving Text Generation
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan +1
Differentially-private mechanisms for text generation typically add carefully calibrated noise to input words and use the nearest neighbor to the noised input as the output word. W…
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…