| Privacy amplification by subsampling: Tight analyses via couplings and divergences B Balle, G Barthe, M Gaboardi Advances in neural information processing systems 31, 2018 | 444 | 2018 |
| Differential privacy: An economic method for choosing epsilon J Hsu, M Gaboardi, A Haeberlen, S Khanna, A Narayan, BC Pierce, ... 2014 IEEE 27th Computer Security Foundations Symposium, 398-410, 2014 | 415 | 2014 |
| Differential privacy: A primer for a non-technical audience A Wood, M Altman, A Bembenek, M Bun, M Gaboardi, J Honaker, ... Vand. J. Ent. & Tech. L. 21, 209, 2018 | 349 | 2018 |
| Software foundations BC Pierce, C Casinghino, M Gaboardi, M Greenberg, C Hriţcu, V Sjöberg, ... Webpage: http://www. cis. upenn. edu/bcpierce/sf/current/index. html 16, 2010 | 260* | 2010 |
| Linear dependent types for differential privacy M Gaboardi, A Haeberlen, J Hsu, A Narayan, BC Pierce Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of …, 2013 | 240 | 2013 |
| Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing M Gaboardi, H Lim, R Rogers, S Vadhan International conference on machine learning, 2111-2120, 2016 | 167 | 2016 |
| Linear dependent types and relative completeness U Dal Lago, M Gaboardi Logic in Computer Science (LICS), 2011 26th Annual IEEE Symposium on, 133-142, 2011 | 154 | 2011 |
| A core quantitative coeffect calculus A Brunel, M Gaboardi, D Mazza, S Zdancewic European Symposium on Programming Languages and Systems, 351-370, 2014 | 138 | 2014 |
| Hypothesis testing interpretations and renyi differential privacy B Balle, G Barthe, M Gaboardi, J Hsu, T Sato International Conference on Artificial Intelligence and Statistics, 2496-2506, 2020 | 121 | 2020 |
| Dual query: Practical private query release for high dimensional data M Gaboardi, EJG Arias, J Hsu, A Roth, ZS Wu International Conference on Machine Learning, 1170-1178, 2014 | 118 | 2014 |
| Proving differential privacy via probabilistic couplings G Barthe, M Gaboardi, B Grégoire, J Hsu, PY Strub Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer …, 2016 | 114 | 2016 |
| Higher-order approximate relational refinement types for mechanism design and differential privacy G Barthe, M Gaboardi, EJ Gallego Arias, J Hsu, A Roth, PY Strub Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of …, 2015 | 112 | 2015 |
| Combining effects and coeffects via grading M Gaboardi, S Katsumata, D Orchard, F Breuvart, T Uustalu ACM SIGPLAN Notices 51 (9), 476-489, 2016 | 110 | 2016 |
| Psi ({\Psi}): a private data sharing interface M Gaboardi, J Honaker, G King, J Murtagh, K Nissim, J Ullman, S Vadhan arXiv preprint arXiv:1609.04340, 2016 | 102 | 2016 |
| Relational cost analysis E Çiçek, G Barthe, M Gaboardi, D Garg, J Hoffmann ACM SIGPLAN Notices 52 (1), 316-329, 2017 | 98 | 2017 |
| Bridging the gap between computer science and legal approaches to privacy K Nissim, A Bembenek, A Wood, M Bun, M Gaboardi, U Gasser, ... Harv. JL & Tech. 31, 687, 2017 | 97 | 2017 |
| Empirical risk minimization in non-interactive local differential privacy revisited D Wang, M Gaboardi, J Xu Advances in Neural Information Processing Systems 31, 2018 | 77 | 2018 |
| Local private hypothesis testing: Chi-square tests M Gaboardi, R Rogers International Conference on Machine Learning, 1626-1635, 2018 | 75 | 2018 |
| A relational logic for higher-order programs A Aguirre, G Barthe, M Gaboardi, D Garg, PY Strub Proceedings of the ACM on Programming Languages 1 (ICFP), 1-29, 2017 | 74 | 2017 |
| Covariance-aware private mean estimation without private covariance estimation G Brown, M Gaboardi, A Smith, J Ullman, L Zakynthinou Advances in neural information processing systems 34, 7950-7964, 2021 | 72 | 2021 |
Gilles BartheMPI-SP and IMDEA Software InstituteEmail confirmado em mpi-sp.org