Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Nicolas Mayoraz; Hema Venkata Krishna Giri Narra; Steffen Rendle; Amer Sinha; et al. (AdKDD@KDD, 2024)

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (CoRR, 2024)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; S. Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Vikas Sahu (CoRR, 2024)

Differentially Private Ad Conversion Measurement. - John Delaney; Badih Ghazi; Charlie Harrison; Christina Ilvento; Ravi Kumar 001; Pasin Manurangsi; Martin Pál; Karthik Prabhakar; Mariana Raykova 001 (Proc. Priv. Enhancing Technol., 2024)

Balls-and-Bins Sampling for DP-SGD. - Lynn Chua; Badih Ghazi; Charlie Harrison; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (AISTATS, 2025)

Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime. - Charlie Harrison; Pasin Manurangsi (FORC, 2025)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Mariana Raykova 001; Vikas Sahu; Phillipp Schoppmann (Proc. Priv. Enhancing Technol., 2025)