Luca Melis
Cited by
Cited by
Exploiting unintended feature leakage in collaborative learning
L Melis, C Song, E De Cristofaro, V Shmatikov
2019 IEEE symposium on security and privacy (SP), 691-706, 2019
Logan: Membership inference attacks against generative models
J Hayes, L Melis, G Danezis, E De Cristofaro
arXiv preprint arXiv:1705.07663, 2017
Differentially private mixture of generative neural networks
G Acs, L Melis, C Castelluccia, E De Cristofaro
IEEE Transactions on Knowledge and Data Engineering 31 (6), 1109-1121, 2018
Efficient private statistics with succinct sketches
L Melis, G Danezis, E De Cristofaro
NDSS 2016, 2015
Differentially private query release through adaptive projection
S Aydore, W Brown, M Kearns, K Kenthapadi, L Melis, A Roth, AA Siva
International Conference on Machine Learning, 457-467, 2021
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
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
Adversarial robustness with non-uniform perturbations
E Erdemir, J Bickford, L Melis, S Aydore
Advances in Neural Information Processing Systems 34, 19147-19159, 2021
Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity
K Maeng, H Lu, L Melis, J Nguyen, M Rabbat, CJ Wu
Proceedings of the 16th ACM Conference on Recommender Systems, 156-167, 2022
Measuring and Privately Building Highly Predictive Blacklisting
L Melis, A Pyrgelis, E De Cristofaro
Federated linear contextual bandits with user-level differential privacy
R Huang, H Zhang, L Melis, M Shen, M Hejazinia, J Yang
International Conference on Machine Learning, 14060-14095, 2023
Detecting anomalous events using autoencoders
B Coskun, W Ding, L Melis
US Patent 11,374,952, 2022
Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems.
M Hejazinia, D Huba, I Leontiadis, K Maeng, M Malek, L Melis, I Mironov, ...
IEEE Data Eng. Bull. 46 (1), 145-157, 2023
EXACT: Extensive Attack for Split Learning
X Qiu, I Leontiadis, L Melis, A Sablayrolles, P Stock
arXiv preprint arXiv:2305.12997, 2023
Detecting anomalous events from categorical data using autoencoders
S Aydore, B Coskun, L Melis
US Patent 11,537,902, 2022
Have Missing Data? Make It Miss More! Imputing Tabular Data with Masked Autoencoding
T Du, L Melis, T Wang
Building and evaluating privacy-preserving data processing systems
L Melis
UCL (University College London), 2018
Fast privacy-preserving network function outsourcing
HJ Asghar, E De Cristofaro, G Jourjon, MA Kaafar, L Mathy, L Melis, ...
Computer Networks 163, 106893, 2019
Generating relaxed synthetic data using adaptive projection
S Aydore, W Brown, M Kearns, K Kenthapadi, L Melis, A Roth, AA Siva
US Patent 11,841,863, 2023
Supplementary Material for Adversarial Robustness with Non-uniform Perturbations
E Erdemir, J Bickford, L Melis, S Aydöre
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