| Analyzing federated learning through an adversarial lens AN Bhagoji, S Chakraborty, P Mittal, S Calo International Conference on Machine Learning, 634-643, 2019 | 578 | 2019 |
| Interpretability of deep learning models: A survey of results S Chakraborty, R Tomsett, R Raghavendra, D Harborne, M Alzantot, ... 2017 IEEE smartworld, ubiquitous intelligence & computing, advanced …, 2017 | 324 | 2017 |
| Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples. C Liu, S Chakraborty, P Mittal NDSS 16, 21-24, 2016 | 207 | 2016 |
| Genattack: Practical black-box attacks with gradient-free optimization M Alzantot, Y Sharma, S Chakraborty, H Zhang, CJ Hsieh, MB Srivastava Proceedings of the Genetic and Evolutionary Computation Conference, 1111-1119, 2019 | 182 | 2019 |
| Neighborhood based fast graph search in large networks A Khan, N Li, X Yan, Z Guan, S Chakraborty, S Tao Proceedings of the 2011 ACM SIGMOD International Conference on Management of …, 2011 | 170 | 2011 |
| Interpretable to whom? A role-based model for analyzing interpretable machine learning systems R Tomsett, D Braines, D Harborne, A Preece, S Chakraborty arXiv preprint arXiv:1806.07552, 2018 | 140 | 2018 |
| Stakeholders in explainable AI A Preece, D Harborne, D Braines, R Tomsett, S Chakraborty arXiv preprint arXiv:1810.00184, 2018 | 134 | 2018 |
| Sensegen: A deep learning architecture for synthetic sensor data generation M Alzantot, S Chakraborty, M Srivastava 2017 IEEE International Conference on Pervasive Computing and Communications …, 2017 | 128 | 2017 |
| {ipShield}: A Framework For Enforcing {Context-Aware} Privacy S Chakraborty, C Shen, KR Raghavan, Y Shoukry, M Millar, M Srivastava 11th USENIX symposium on networked systems design and implementation (NSDI …, 2014 | 102 | 2014 |
| Compressive oversampling for robust data transmission in sensor networks Z Charbiwala, S Chakraborty, S Zahedi, T He, C Bisdikian, Y Kim, ... 2010 Proceedings IEEE INFOCOM, 1-9, 2010 | 75 | 2010 |
| A framework for context-aware privacy of sensor data on mobile systems S Chakraborty, KR Raghavan, MP Johnson, MB Srivastava Proceedings of the 14th Workshop on Mobile Computing Systems and …, 2013 | 71 | 2013 |
| Fair transfer learning with missing protected attributes A Coston, KN Ramamurthy, D Wei, KR Varshney, S Speakman, ... Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 91-98, 2019 | 65 | 2019 |
| Rapid trust calibration through interpretable and uncertainty-aware AI R Tomsett, A Preece, D Braines, F Cerutti, S Chakraborty, M Srivastava, ... Patterns 1 (4), 100049, 2020 | 61 | 2020 |
| Blockchain analytics and artificial intelligence DN Dillenberger, P Novotny, Q Zhang, P Jayachandran, H Gupta, S Hans, ... IBM Journal of Research and Development 63 (2/3), 5: 1-5: 14, 2019 | 61 | 2019 |
| Ibm federated learning: an enterprise framework white paper v0. 1 H Ludwig, N Baracaldo, G Thomas, Y Zhou, A Anwar, S Rajamoni, Y Ong, ... arXiv preprint arXiv:2007.10987, 2020 | 60 | 2020 |
| Sanity checks for saliency metrics R Tomsett, D Harborne, S Chakraborty, P Gurram, A Preece Proceedings of the AAAI conference on artificial intelligence 34 (04), 6021-6029, 2020 | 60 | 2020 |
| Sensorsafe: a framework for privacy-preserving management of personal sensory information H Choi, S Chakraborty, ZM Charbiwala, MB Srivastava Workshop on Secure Data Management, 85-100, 2011 | 58 | 2011 |
| Generative policy model for autonomic management D Verma, S Calo, S Chakraborty, E Bertino, C Williams, J Tucker, B Rivera 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced …, 2017 | 44 | 2017 |
| PrOLoc: Resilient localization with private observers using partial homomorphic encryption A Alanwar, Y Shoukry, S Chakraborty, P Martin, P Tabuada, M Srivastava 2017 16th ACM/IEEE International Conference on Information Processing in …, 2017 | 42 | 2017 |
| Model poisoning attacks in federated learning AN Bhagoji, S Chakraborty, P Mittal, S Calo Proc. Workshop Secur. Mach. Learn.(SecML) 32nd Conf. Neural Inf. Process …, 2018 | 37 | 2018 |
