Abhradeep Thakurta

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 68

Top Topics

Differentially Private

Differential Privacy

Top Venues

IEEE Symposium on Security and Privacy

Publications

Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta
Optimal Rates for DP-SCO with a Single Epoch and Large Batches. CoRR (2024)
Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Thakurta
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. CoRR (2024)
Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. AISTATS (2024)
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Learning with Public Features. AISTATS (2024)
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR (2024)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
Why Is Public Pretraining Necessary for Private Model Training? CoRR (2023)
Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Privacy Amplification for Matrix Mechanisms. CoRR (2023)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. CoRR (2023)
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR (2023)
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Learning with Public Features. CoRR (2023)
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta
Differentially Private and Lazy Online Convex Optimization. COLT (2023)
Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
Training Private Models That Know What They Don't Know. CoRR (2023)
Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek
Differentially Private Stream Processing at Scale. CoRR (2023)
Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. COLT (2023)
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Challenges towards the Next Frontier in Privacy. CoRR (2023)
Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR (2023)
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR (2022)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. CoRR (2022)
Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization. CoRR (2022)
Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi
Private Matrix Approximation and Geometry of Unitary Orbits. COLT (2022)
Adam D. Smith, Abhradeep Thakurta
Fully Adaptive Composition for Gaussian Differential Privacy. CoRR (2022)
Harsh Mehta, Walid Krichene, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
Differentially Private Image Classification from Features. CoRR (2022)
Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot, Abhradeep Thakurta
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search. CoRR (2022)
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
(Nearly) Optimal Private Linear Regression via Adaptive Clipping. CoRR (2022)
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta
Toward Training at ImageNet Scale with Differential Privacy. CoRR (2022)
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
Large Scale Transfer Learning for Differentially Private Image Classification. CoRR (2022)
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. COLT (2022)
Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. CoRR (2022)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. ICML (2022)
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. ICML (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR (2021)
Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS (2021)
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML (2021)
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. CoRR (2021)
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR (2021)
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. AAAI (2021)
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr
Is Private Learning Possible with Instance Encoding? IEEE Symposium on Security and Privacy (2021)
Peter Kairouz, Mónica Ribero Diaz, Keith Rush, Abhradeep Thakurta
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. COLT (2021)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. CoRR (2021)
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. CoRR (2020)
Adam D. Smith, Shuang Song, Abhradeep Thakurta
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. NeurIPS (2020)
Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR (2020)
Peter Kairouz, Mónica Ribero, Keith Rush, Abhradeep Thakurta
Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. CoRR (2020)
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta
Obliviousness Makes Poisoning Adversaries Weaker. CoRR (2020)
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. NeurIPS (2020)
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. CoRR (2020)
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR (2020)
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. CoRR (2020)
Shuang Song, Om Thakkar, Abhradeep Thakurta
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. CoRR (2020)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21 (2020)
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta
Private Stochastic Convex Optimization with Optimal Rates. CoRR (2019)
Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, Lun Wang
Towards Practical Differentially Private Convex Optimization. IEEE Symposium on Security and Privacy (2019)
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. SODA (2019)
Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta
Privacy-preserving Data Mining in Industry. WWW (Companion Volume) (2019)
Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta
Privacy Amplification by Iteration. FOCS (2018)
Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma
Erasure-Resilient Property Testing. SIAM J. Comput. 47 (2) (2018)
Raef Bassily, Om Thakkar, Abhradeep Thakurta
Model-Agnostic Private Learning via Stability. CoRR (2018)
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. CoRR (2018)
Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta
Privacy Amplification by Iteration. CoRR (2018)
Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta
Differentially Private Matrix Completion Revisited. ICML (2018)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. CoRR (2017)
Prateek Jain, Om Thakkar, Abhradeep Thakurta
Differentially Private Matrix Completion, Revisited. CoRR (2017)
Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay
Is Interaction Necessary for Distributed Private Learning? IEEE Symposium on Security and Privacy (2017)
Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma
Erasure-Resilient Property Testing. CoRR (2016)
Kashyap Dixit, Sofya Raskhodnikova, Abhradeep Thakurta, Nithin Varma
Erasure-Resilient Property Testing. ICALP (2016)
Kunal Talwar, Abhradeep Thakurta, Li Zhang
Nearly Optimal Private LASSO. NIPS (2015)
Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, Oliver Williams
To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout. CoRR (2015)
Nikita Mishra, Abhradeep Thakurta
(Nearly) Optimal Differentially Private Stochastic Multi-Arm Bandits. UAI (2015)
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang
Analyze gauss: optimal bounds for privacy-preserving principal component analysis. STOC (2014)
Raef Bassily, Adam D. Smith, Abhradeep Thakurta
Private Empirical Risk Minimization, Revisited. CoRR (2014)
Kunal Talwar, Abhradeep Thakurta, Li Zhang
Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry. CoRR (2014)
Raef Bassily, Adam D. Smith, Abhradeep Thakurta
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. FOCS (2014)
Prateek Jain, Abhradeep Thakurta
Differentially Private Learning with Kernels. ICML (3) (2013)
Abhradeep Thakurta, Adam D. Smith
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. COLT (2013)
Kashyap Dixit, Madhav Jha, Sofya Raskhodnikova, Abhradeep Thakurta
Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy. TCC (2013)
Prateek Jain, Abhradeep Thakurta
Mirror Descent Based Database Privacy. APPROX-RANDOM (2012)
Daniel Kifer, Adam D. Smith, Abhradeep Thakurta
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. COLT (2012)
Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, David E. Culler
GUPT: privacy preserving data analysis made easy. SIGMOD Conference (2012)
Prateek Jain, Pravesh Kothari, Abhradeep Thakurta
Differentially Private Online Learning. COLT (2012)
Kashyap Dixit, Madhav Jha, Abhradeep Thakurta
Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy CoRR (2012)
Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta
Noiseless Database Privacy. IACR Cryptol. ePrint Arch. 2011 (2011)
Prateek Jain, Pravesh Kothari, Abhradeep Thakurta
Differentially Private Online Learning CoRR (2011)
Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta
Noiseless Database Privacy. ASIACRYPT (2011)
Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta
Discovering frequent patterns in sensitive data. KDD (2010)