| 2024 | ISIT | Utilitarian Privacy and Private Sampling. | Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran |
| 2023 | ICLR | A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. | Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2022 | AISTATS | Flexible Accuracy for Differential Privacy. | Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran |
| 2022 | ISIT | Distributed User-Level Private Mean Estimation. | Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2021 | AISTATS | Shuffled Model of Differential Privacy in Federated Learning. | Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh |
| 2021 | CCS | On the Rnyi Differential Privacy of the Shuffle Model. | Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz |
| 2021 | ICML | Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. | Deepesh Data, Suhas N. Diggavi |
| 2021 | ISIT | Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. | Deepesh Data, Suhas N. Diggavi |
| 2021 | ISIT | Differentially Private Federated Learning with Shuffling and Client Self-Sampling. | Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2021 | ISIT | SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. | Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi |
| 2020 | ISIT | On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent. | Deepesh Data, Suhas N. Diggavi |
| 2020 | ISIT | Hiding Identities: Estimation Under Local Differential Privacy. | Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2019 | ISIT | Byzantine-Tolerant Distributed Coordinate Descent. | Deepesh Data, Suhas N. Diggavi |
| 2019 | ISIT | Data Encoding Methods for Byzantine-Resilient Distributed Optimization. | Deepesh Data, Linqi Song, Suhas N. Diggavi |
| 2018 | CRYPTO | Must the Communication Graph of MPC Protocols be an Expander? | Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubcek |
| 2018 | PKC | Towards Characterizing Securely Computable Two-Party Randomized Functions. | Deepesh Data, Manoj Prabhakaran |
| 2017 | ITW | Secure computation of randomized functions: Further results. | Deepesh Data, Vinod M. Prabhakaran |
| 2016 | ISIT | Secure computation of randomized functions. | Deepesh Data |
| 2015 | ISIT | On coding for secure computing. | Deepesh Data, Vinod M. Prabhakaran |
| 2014 | CRYPTO | On the Communication Complexity of Secure Computation. | Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran |
| 2014 | ITW | How to securely compute the modulo-two sum of binary sources. | Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran |