9 citations · 38 across the 15 of their papers we have counts for
8 papers · 1 filter
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,…
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.…
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…
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data…
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake +1
Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presen…
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Oluwaseyi Feyisetan, Thomas Drake, Borja Balle +1
Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for la…