| Efficient private statistics with succinct sketches L Melis, G Danezis, E De Cristofaro arXiv preprint arXiv:1508.06110, 2015 | 29 | 2015 |
| LOGAN: Membership inference attacks against generative models J Hayes, L Melis, G Danezis, E De Cristofaro Proceedings on Privacy Enhancing Technologies 2019 (1), 133-152, 2019 | 28* | 2019 |
| Private processing of outsourced network functions: Feasibility and constructions L Melis, HJ Asghar, E De Cristofaro, MA Kaafar Proceedings of the 2016 ACM International Workshop on Security in Software …, 2016 | 22 | 2016 |
| Splitbox: Toward efficient private network function virtualization HJ Asghar, L Melis, C Soldani, E De Cristofaro, MA Kaafar, L Mathy Proceedings of the 2016 workshop on Hot topics in Middleboxes and Network …, 2016 | 17 | 2016 |
| Exploiting unintended feature leakage in collaborative learning L Melis, C Song, E De Cristofaro, V Shmatikov Exploiting Unintended Feature Leakage in Collaborative Learning, 0, 2018 | 14* | 2018 |
| Differentially private mixture of generative neural networks G Acs, L Melis, C Castelluccia, E De Cristofaro IEEE Transactions on Knowledge and Data Engineering, 2018 | 12 | 2018 |
| Building and measuring privacy-preserving predictive blacklists L Melis, A Pyrgelis, E De Cristofaro arXiv preprint arXiv:1512.04114, 2015 | 1 | 2015 |
| Building and evaluating privacy-preserving data processing systems L Melis UCL (University College London), 2018 | | 2018 |