Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent. - Lauren Watson; Benedek Rozemberczki; Rik Sarkar (CoRR, 2020)

Privacy Preserving Detection of Path Bias Attacks in Tor. - Lauren Watson; Anupam Mediratta; Tariq Elahi; Rik Sarkar (Proc. Priv. Enhancing Technol., 2020)

On the Importance of Difficulty Calibration in Membership Inference Attacks. - Lauren Watson; Chuan Guo 001; Graham Cormode; Alexandre Sablayrolles (ICLR, 2022)

The Shapley Value in Machine Learning. - Benedek Rozemberczki; Lauren Watson; Péter Bayer; Hao-Tsung Yang; Oliver Kiss; Sebastian Nilsson; Rik Sarkar (IJCAI, 2022)

Continual and Sliding Window Release for Private Empirical Risk Minimization. - Lauren Watson; Abhirup Ghosh; Benedek Rozemberczki; Rik Sarkar (CoRR, 2022)

Differentially Private Shapley Values for Data Evaluation. - Lauren Watson; Rayna Andreeva; Hao-Tsung Yang; Rik Sarkar (CoRR, 2022)

Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation? - Gonzalo Martínez 001; Lauren Watson; Pedro Reviriego; José Alberto Hernández 001; Marc Juarez; Rik Sarkar (CoRR, 2023)

Accelerated Shapley Value Approximation for Data Evaluation. - Lauren Watson; Zeno Kujawa; Rayna Andreeva; Hao-Tsung Yang; Tariq Elahi; Rik Sarkar (CoRR, 2023)

Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent. - Lauren Watson; Eric Gan; Mohan Dantam; Baharan Mirzasoleiman; Rik Sarkar (CoRR, 2023)

Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet. - Gonzalo Martínez 001; Lauren Watson; Pedro Reviriego; José Alberto Hernández 001; Marc Juarez; Rik Sarkar (Epi UAI, 2024)