Improved Upper Bounds on a'(G☐H). - Punit Mehta; Rahul Muthu; Gaurav Patel; Om Thakkar 001; Devanshi Vyas (CoRR, 2015)

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. - Ryan M. Rogers; Aaron Roth 001; Adam D. Smith 001; Om Thakkar 001 (FOCS, 2016)

Differentially Private Matrix Completion Revisited. - Prateek Jain 002; Om Dipakbhai Thakkar; Abhradeep Thakurta (ICML, 2018)

Model-Agnostic Private Learning. - Raef Bassily; Abhradeep Guha Thakurta; Om Dipakbhai Thakkar (NeurIPS, 2018)

Model-Agnostic Private Learning via Stability. - Raef Bassily; Om Thakkar 001; Abhradeep Thakurta (CoRR, 2018)

Towards Practical Differentially Private Convex Optimization. - Roger Iyengar; Joseph P. Near; Dawn Song; Om Thakkar 001; Abhradeep Thakurta; Lun Wang 001 (IEEE Symposium on Security and Privacy, 2019)

Differentially Private Learning with Adaptive Clipping. - Om Thakkar 001; Galen Andrew; H. Brendan McMahan (CoRR, 2019)

Advances in privacy-preserving machine learning - Om Dipakbhai Thakkar (2019)

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. - Ryan Rogers 002; Aaron Roth 001; Adam D. Smith 001; Nathan Srebro; Om Thakkar 001; Blake E. Woodworth (AISTATS, 2020)

Privacy Amplification via Random Check-Ins. - Borja Balle; Peter Kairouz; Brendan McMahan; Om Dipakbhai Thakkar; Abhradeep Thakurta (NeurIPS, 2020)

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. - Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta (CoRR, 2020)

Understanding Unintended Memorization in Federated Learning. - Om Thakkar 001; Swaroop Ramaswamy; Rajiv Mathews; Françoise Beaufays (CoRR, 2020)

Training Production Language Models without Memorizing User Data. - Swaroop Ramaswamy; Om Thakkar 001; Rajiv Mathews; Galen Andrew; H. Brendan McMahan; Françoise Beaufays (CoRR, 2020)

Evading the Curse of Dimensionality in Unconstrained Private GLMs. - Shuang Song 001; Thomas Steinke 002; Om Thakkar 001; Abhradeep Thakurta (AISTATS, 2021)

Practical and Private (Deep) Learning Without Sampling or Shuffling. - Peter Kairouz; Brendan McMahan; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Zheng Xu 002 (ICML, 2021)

Differentially Private Learning with Adaptive Clipping. - Galen Andrew; Om Thakkar 001; Brendan McMahan; Swaroop Ramaswamy (NeurIPS, 2021)

Revealing and Protecting Labels in Distributed Training. - Trung Dang 002; Om Thakkar 001; Swaroop Ramaswamy; Rajiv Mathews; Peter Chin 001; Françoise Beaufays (NeurIPS, 2021)

The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. - Shubhankar Mohapatra; Sajin Sasy; Xi He 001; Gautam Kamath 001; Om Thakkar 001 (AAAI, 2022)

A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT. - Trung Dang 002; Om Thakkar 001; Swaroop Ramaswamy; Rajiv Mathews; Peter Chin 001; Françoise Beaufays (ICASSP, 2022)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

Extracting Targeted Training Data from ASR Models, and How to Mitigate It. - Ehsan Amid; Om Dipakbhai Thakkar; Arun Narayanan; Rajiv Mathews; Françoise Beaufays (INTERSPEECH, 2022)

Detecting Unintended Memorization in Language-Model-Fused ASR. - W. Ronny Huang; Steve Chien; Om Dipakbhai Thakkar; Rajiv Mathews (INTERSPEECH, 2022)

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Om Thakkar 001; Abhradeep Thakurta (CoRR, 2022)

Measuring Forgetting of Memorized Training Examples. - Matthew Jagielski; Om Thakkar 001; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song 001; Abhradeep Guha Thakurta; Nicolas Papernot; Chiyuan Zhang (ICLR, 2023)

Why Is Public Pretraining Necessary for Private Model Training? - Arun Ganesh; Mahdi Haghifam; Milad Nasr; Sewoong Oh; Thomas Steinke 002; Om Thakkar 001; Abhradeep Guha Thakurta; Lun Wang 001 (ICML, 2023)

Noise Masking Attacks and Defenses for Pretrained Speech Models. - Matthew Jagielski; Om Thakkar 001; Lun Wang 001 (ICASSP, 2024)

Unintended Memorization in Large ASR Models, and How to Mitigate It. - Lun Wang 001; Om Thakkar 001; Rajiv Mathews (ICASSP, 2024)

Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping. - Lun Wang 001; Om Thakkar 001; Zhong Meng; Nicole Rafidi; Rohit Prabhavalkar; Arun Narayanan (INTERSPEECH, 2024)

Quantifying Unintended Memorization in BEST-RQ ASR Encoders. - Virat Shejwalkar; Om Thakkar 001; Arun Narayanan (INTERSPEECH, 2024)

Training Large ASR Encoders With Differential Privacy. - Geeticka Chauhan; Steve Chien; Om Thakkar 001; Abhradeep Thakurta; Arun Narayanan (SLT, 2024)

Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models. - Hongbin Liu; Lun Wang 001; Om Thakkar 001; Abhradeep Thakurta; Arun Narayanan (CoRR, 2024)

Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Yarong Mu; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Xinyi Zheng (Proc. Priv. Enhancing Technol., 2025)