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Journal Articles
- 2023
[j10]Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek:
Must the Communication Graph of MPC Protocols be an Expander? J. Cryptol. 36(3): 20 (2023)- [j9]Navjot Singh
, Deepesh Data, Jemin George, Suhas N. Diggavi:
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. IEEE Trans. Autom. Control. 68(2): 721-736 (2023) - [j8]Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient High-Dimensional Federated Learning. IEEE Trans. Inf. Theory 69(10): 6639-6670 (2023) - 2021
- [j7]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory 2(1): 464-478 (2021) - [j6]Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi:
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. IEEE J. Sel. Areas Inf. Theory 2(3): 954-969 (2021) - [j5]Deepesh Data, Linqi Song, Suhas N. Diggavi:
Data Encoding for Byzantine-Resilient Distributed Optimization. IEEE Trans. Inf. Theory 67(2): 1117-1140 (2021) - 2020
- [j4]Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi:
Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations. IEEE J. Sel. Areas Inf. Theory 1(1): 217-226 (2020) - [j3]Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi:
Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory 1(3): 745-759 (2020) - [j2]Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran:
Interactive Secure Function Computation. IEEE Trans. Inf. Theory 66(9): 5492-5521 (2020) - 2016
- [j1]Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran:
Communication and Randomness Lower Bounds for Secure Computation. IEEE Trans. Inf. Theory 62(7): 3901-3929 (2016)
Conference and Workshop Papers
- 2024
- [c28]Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran:
Utilitarian Privacy and Private Sampling. ISIT 2024: 392-397 - 2023
- [c27]Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. ICLR 2023 - 2022
- [c26]Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran:
Flexible Accuracy for Differential Privacy. AISTATS 2022: 3847-3882 - [c25]Yanwen Mao, Deepesh Data, Suhas N. Diggavi, Paulo Tabuada:
Decentralized Learning Robust to Data Poisoning Attacks. CDC 2022: 6788-6793 - [c24]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
Distributed User-Level Private Mean Estimation. ISIT 2022: 2196-2201 - 2021
- [c23]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Differential Privacy in Federated Learning. AISTATS 2021: 2521-2529 - [c22]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz:
On the Rényi Differential Privacy of the Shuffle Model. CCS 2021: 2321-2341 - [c21]Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. ICML 2021: 2478-2488 - [c20]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
Differentially Private Federated Learning with Shuffling and Client Self-Sampling. ISIT 2021: 338-343 - [c19]Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi:
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. ISIT 2021: 1212-1217 - [c18]Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. ISIT 2021: 2310-2315 - [c17]Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi:
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. NeurIPS 2021: 3622-3634 - [c16]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. NeurIPS 2021: 29181-29192 - 2020
- [c15]Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi:
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. CDC 2020: 3449-3456 - [c14]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
Hiding Identities: Estimation Under Local Differential Privacy. ISIT 2020: 914-919 - [c13]Deepesh Data, Suhas N. Diggavi:
On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent. ISIT 2020: 2628-2633 - 2019
- [c12]Deepesh Data, Linqi Song, Suhas N. Diggavi:
Data Encoding Methods for Byzantine-Resilient Distributed Optimization. ISIT 2019: 2719-2723 - [c11]Deepesh Data, Suhas N. Diggavi:
Byzantine-Tolerant Distributed Coordinate Descent. ISIT 2019: 2724-2728 - [c10]Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi:
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. NeurIPS 2019: 14668-14679 - 2018
- [c9]Deepesh Data, Linqi Song, Suhas N. Diggavi:
Data Encoding for Byzantine-Resilient Distributed Gradient Descent. Allerton 2018: 863-870 - [c8]Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek:
Must the Communication Graph of MPC Protocols be an Expander? CRYPTO (3) 2018: 243-272 - [c7]Deepesh Data, Manoj Prabhakaran:
Towards Characterizing Securely Computable Two-Party Randomized Functions. Public Key Cryptography (1) 2018: 675-697 - 2017
- [c6]Deepesh Data, Vinod M. Prabhakaran:
Secure computation of randomized functions: Further results. ITW 2017: 264-268 - 2016
- [c5]Deepesh Data:
Secure computation of randomized functions. ISIT 2016: 3053-3057 - 2015
- [c4]Deepesh Data, Vinod M. Prabhakaran:
On coding for secure computing. ISIT 2015: 2737-2741 - 2014
- [c3]Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran:
On the Communication Complexity of Secure Computation. CRYPTO (2) 2014: 199-216 - [c2]Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran:
How to securely compute the modulo-two sum of binary sources. ITW 2014: 496-500 - 2013
- [c1]Deepesh Data, Vinod M. Prabhakaran:
Communication requirements for secure computation. Allerton 2013: 211-217
Informal and Other Publications
- 2023
- [i25]Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek:
Must the Communication Graph of MPC Protocols be an Expander? CoRR abs/2305.11428 (2023) - 2022
- [i24]Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy. CoRR abs/2207.01771 (2022) - 2021
- [i23]Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi:
QuPeL: Quantized Personalization with Applications to Federated Learning. CoRR abs/2102.11786 (2021) - [i22]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz:
On the Renyi Differential Privacy of the Shuffle Model. CoRR abs/2105.05180 (2021) - [i21]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i20]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning. CoRR abs/2107.08763 (2021) - [i19]Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi:
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. CoRR abs/2107.13892 (2021) - [i18]Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran:
Flexible Accuracy for Differential Privacy. CoRR abs/2110.09580 (2021) - 2020
- [i17]Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi:
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. CoRR abs/2005.07041 (2020) - [i16]Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. CoRR abs/2005.07866 (2020) - [i15]Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi:
Successive Refinement of Privacy. CoRR abs/2005.11651 (2020) - [i14]Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. CoRR abs/2006.13041 (2020) - [i13]Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh:
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR abs/2008.07180 (2020) - 2019
- [i12]Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi:
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations. CoRR abs/1906.02367 (2019) - [i11]Deepesh Data, Linqi Song, Suhas N. Diggavi:
Data Encoding for Byzantine-Resilient Distributed Optimization. CoRR abs/1907.02664 (2019) - [i10]Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi:
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization. CoRR abs/1910.14280 (2019) - 2018
- [i9]Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran:
Interactive Secure Function Computation. CoRR abs/1812.03838 (2018) - [i8]Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek:
Must the Communication Graph of MPC Protocols be an Expander? IACR Cryptol. ePrint Arch. 2018: 540 (2018) - 2017
- [i7]Deepesh Data, Vinod M. Prabhakaran:
Secure Computation of Randomized Functions: Further Results. CoRR abs/1705.07081 (2017) - [i6]Deepesh Data, Manoj Prabhakaran:
Towards Characterizing Securely Computable Two-Party Randomized Functions. IACR Cryptol. ePrint Arch. 2017: 692 (2017) - 2016
- [i5]Deepesh Data:
Secure Computation of Randomized Functions. CoRR abs/1601.06562 (2016) - 2015
- [i4]Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran:
Communication and Randomness Lower Bounds for Secure Computation. CoRR abs/1512.07735 (2015) - [i3]Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran:
On the Communication Complexity of Secure Computation. IACR Cryptol. ePrint Arch. 2015: 391 (2015) - 2014
- [i2]Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran:
How to Securely Compute the Modulo-Two Sum of Binary Sources. CoRR abs/1405.2555 (2014) - 2013
- [i1]Deepesh Data, Vinod M. Prabhakaran, Manoj Prabhakaran:
On the Communication Complexity of Secure Computation. CoRR abs/1311.7584 (2013)
Coauthor Index
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