Communication requirements for secure computation. - Deepesh Data; Vinod M. Prabhakaran (Allerton, 2013)

How to securely compute the modulo-two sum of binary sources. - Deepesh Data; Bikash Kumar Dey; Manoj Mishra; Vinod M. Prabhakaran (ITW, 2014)

On coding for secure computing. - Deepesh Data; Vinod M. Prabhakaran (ISIT, 2015)

On the Communication Complexity of Secure Computation. - Deepesh Data; Manoj Prabhakaran 001; Vinod M. Prabhakaran (IACR Cryptol. ePrint Arch., 2015)

Secure computation of randomized functions. - Deepesh Data (ISIT, 2016)

Communication and Randomness Lower Bounds for Secure Computation. - Deepesh Data; Vinod M. Prabhakaran; Manoj M. Prabhakaran 001 (IEEE Trans. Inf. Theory, 2016)

Secure computation of randomized functions: Further results. - Deepesh Data; Vinod M. Prabhakaran (ITW, 2017)

Towards Characterizing Securely Computable Two-Party Randomized Functions. - Deepesh Data; Manoj Prabhakaran 001 (IACR Cryptol. ePrint Arch., 2017)

Data Encoding for Byzantine-Resilient Distributed Gradient Descent. - Deepesh Data; Linqi Song; Suhas N. Diggavi (Allerton, 2018)

Byzantine-Tolerant Distributed Coordinate Descent. - Deepesh Data; Suhas N. Diggavi (ISIT, 2019)

Data Encoding Methods for Byzantine-Resilient Distributed Optimization. - Deepesh Data; Linqi Song; Suhas N. Diggavi (ISIT, 2019)

SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization. - Navjot Singh; Deepesh Data; Jemin George; Suhas N. Diggavi (CoRR, 2019)

On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent. - Deepesh Data; Suhas N. Diggavi (ISIT, 2020)

Hiding Identities: Estimation Under Local Differential Privacy. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2020)

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (CoRR, 2020)

Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations. - Debraj Basu 001; Deepesh Data; Can Karakus; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2020)

Successive Refinement of Privacy. - Antonious M. Girgis; Deepesh Data; Kamalika Chaudhuri; Christina Fragouli; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2020)

Interactive Secure Function Computation. - Deepesh Data; Gowtham R. Kurri; Jithin Ravi; Vinod M. Prabhakaran (IEEE Trans. Inf. Theory, 2020)

Shuffled Model of Differential Privacy in Federated Learning. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (AISTATS, 2021)

On the Rényi Differential Privacy of the Shuffle Model. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Ananda Theertha Suresh; Peter Kairouz (CCS, 2021)

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. - Deepesh Data; Suhas N. Diggavi (ICML, 2021)

Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. - Deepesh Data; Suhas N. Diggavi (ISIT, 2021)

Differentially Private Federated Learning with Shuffling and Client Self-Sampling. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2021)

Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (NeurIPS, 2021)

QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. - Kaan Ozkara; Navjot Singh; Deepesh Data; Suhas N. Diggavi (NeurIPS, 2021)

QuPeL: Quantized Personalization with Applications to Federated Learning. - Kaan Ozkara; Navjot Singh; Deepesh Data; Suhas N. Diggavi (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (IEEE J. Sel. Areas Inf. Theory, 2021)

SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. - Navjot Singh; Deepesh Data; Jemin George; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2021)

Data Encoding for Byzantine-Resilient Distributed Optimization. - Deepesh Data; Linqi Song; Suhas N. Diggavi (IEEE Trans. Inf. Theory, 2021)

Flexible Accuracy for Differential Privacy. - Aman Bansal; Rahul Chunduru; Deepesh Data; Manoj Prabhakaran 001 (AISTATS, 2022)

Decentralized Learning Robust to Data Poisoning Attacks. - Yanwen Mao; Deepesh Data; Suhas N. Diggavi; Paulo Tabuada (CDC, 2022)

Distributed User-Level Private Mean Estimation. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2022)

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy. - Kaan Ozkara; Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (CoRR, 2022)

A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. - Kaan Ozkara; Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ICLR, 2023)

SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. - Navjot Singh; Deepesh Data; Jemin George; Suhas N. Diggavi (IEEE Trans. Autom. Control., 2023)

Must the Communication Graph of MPC Protocols be an Expander? - Elette Boyle; Ran Cohen; Deepesh Data; Pavel Hubácek (J. Cryptol., 2023)

Byzantine-Resilient High-Dimensional Federated Learning. - Deepesh Data; Suhas N. Diggavi (IEEE Trans. Inf. Theory, 2023)

Utilitarian Privacy and Private Sampling. - Aman Bansal; Rahul Chunduru; Deepesh Data; Manoj Prabhakaran 001 (ISIT, 2024)

Decentralized Optimization Resilient Against Local Data Poisoning Attacks. - Yanwen Mao; Deepesh Data; Suhas N. Diggavi; Paulo Tabuada (IEEE Trans. Autom. Control., 2025)