On Primes, Log-Loss Scores and (No) Privacy. - Abhinav Aggarwal; Zekun Xu; Oluwaseyi Feyisetan; Nathanael Teissier (CoRR, 2020)

Differentially Private Adversarial Robustness Through Randomized Perturbations. - Nan Xu; Oluwaseyi Feyisetan; Abhinav Aggarwal; Zekun Xu; Nathanael Teissier (CoRR, 2020)

A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric. - Zekun Xu; Abhinav Aggarwal; Oluwaseyi Feyisetan; Nathanael Teissier (CoRR, 2020)

Research Challenges in Designing Differentially Private Text Generation Mechanisms. - Oluwaseyi Feyisetan; Abhinav Aggarwal; Zekun Xu; Nathanael Teissier (FLAIRS, 2021)

Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise. - Nan Xu; Oluwaseyi Feyisetan; Abhinav Aggarwal; Zekun Xu; Nathanael Teissier (FLAIRS, 2021)

Label Inference Attacks from Log-loss Scores. - Abhinav Aggarwal; Shiva Prasad Kasiviswanathan; Zekun Xu; Oluwaseyi Feyisetan; Nathanael Teissier (ICML, 2021)

On a Utilitarian Approach to Privacy Preserving Text Generation. - Zekun Xu; Abhinav Aggarwal; Oluwaseyi Feyisetan; Nathanael Teissier (CoRR, 2021)

On Codomain Separability and Label Inference from (Noisy) Loss Functions. - Abhinav Aggarwal; Shiva Prasad Kasiviswanathan; Zekun Xu; Oluwaseyi Feyisetan; Nathanael Teissier (CoRR, 2021)

Reconstructing Test Labels from Noisy Loss Functions. - Abhinav Aggarwal; Shiva Prasad Kasiviswanathan; Zekun Xu; Oluwaseyi Feyisetan; Nathanael Teissier (AISTATS, 2022)

Balancing utility and scalability in metric differential privacy. - Jacob Imola; Shiva Prasad Kasiviswanathan; Stephen White; Abhinav Aggarwal; Nathanael Teissier (UAI, 2022)