Virtual Reality for Assessing Body Image: The Body Image Virtual Reality Scale (Bivrs). - Giuseppe Riva 001; Luca Melis; Mirco Bolzoni (Int. J. Virtual Real., 1996)

Treating Body-Image Disturbances. - Giuseppe Riva 001; Luca Melis; Mirco Bolzoni (Commun. ACM, 1997)

Private Processing of Outsourced Network Functions: Feasibility and Constructions. - Luca Melis; Hassan Jameel Asghar; Emiliano De Cristofaro; Mohamed Ali Kâafar (IACR Cryptol. ePrint Arch., 2015)

Efficient Private Statistics with Succinct Sketches. - Luca Melis; George Danezis; Emiliano De Cristofaro (NDSS, 2016)

SplitBox: Toward Efficient Private Network Function Virtualization. - Hassan Jameel Asghar; Luca Melis; Cyril Soldani; Emiliano De Cristofaro; Mohamed Ali Kâafar; Laurent Mathy (HotMiddlebox@SIGCOMM, 2016)

LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks. - Jamie Hayes; Luca Melis; George Danezis; Emiliano De Cristofaro (CoRR, 2017)

On collaborative predictive blacklisting. - Luca Melis; Apostolos Pyrgelis; Emiliano De Cristofaro (Comput. Commun. Rev., 2018)

Inference Attacks Against Collaborative Learning. - Luca Melis; Congzheng Song; Emiliano De Cristofaro; Vitaly Shmatikov (CoRR, 2018)

Building and evaluating privacy-preserving data processing systems. - Luca Melis (2018)

Exploiting Unintended Feature Leakage in Collaborative Learning. - Luca Melis; Congzheng Song; Emiliano De Cristofaro; Vitaly Shmatikov (IEEE Symposium on Security and Privacy, 2019)

Fast privacy-preserving network function outsourcing. - Hassan Jameel Asghar; Emiliano De Cristofaro; Guillaume Jourjon; Mohamed Ali Kâafar; Laurent Mathy; Luca Melis; Craig Russell; Mang Yu (Comput. Networks, 2019)

LOGAN: Membership Inference Attacks Against Generative Models. - Jamie Hayes; Luca Melis; George Danezis; Emiliano De Cristofaro (Proc. Priv. Enhancing Technol., 2019)

Differentially Private Mixture of Generative Neural Networks. - Gergely Ács; Luca Melis; Claude Castelluccia; Emiliano De Cristofaro (IEEE Trans. Knowl. Data Eng., 2019)

Differentially Private Query Release Through Adaptive Projection. - Sergül Aydöre; William Brown; Michael Kearns; Krishnaram Kenthapadi; Luca Melis; Aaron Roth 001; Amaresh Ankit Siva (ICML, 2021)

Adversarial Robustness with Non-uniform Perturbations. - Ecenaz Erdemir; Jeffrey Bickford; Luca Melis; Sergül Aydöre (NeurIPS, 2021)

Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. - Kiwan Maeng; Haiyu Lu; Luca Melis; John Nguyen; Mike Rabbat; Carole-Jean Wu (RecSys, 2022)

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning. - Meisam Hejazinia; Dzmitry Huba; Ilias Leontiadis; Kiwan Maeng; Mani Malek 001; Luca Melis; Ilya Mironov; Milad Nasr; Kaikai Wang; Carole-Jean Wu (CoRR, 2022)

Federated Linear Contextual Bandits with User-level Differential Privacy. - Ruiquan Huang; Huanyu Zhang; Luca Melis; Milan Shen; Meisam Hejazinia; Jing Yang 002 (ICML, 2023)

EXACT: Extensive Attack for Split Learning. - Xinchi Qiu; Ilias Leontiadis; Luca Melis; Alexandre Sablayrolles; Pierre Stock (CoRR, 2023)

Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems. - Meisam Hejazinia; Dzmitry Huba; Ilias Leontiadis; Kiwan Maeng; Mani Malek 001; Luca Melis; Ilya Mironov; Milad Nasr; Kaikai Wang; Carole-Jean Wu (IEEE Data Eng. Bull., 2023)

ReMasker: Imputing Tabular Data with Masked Autoencoding. - Tianyu Du; Luca Melis; Ting Wang 006 (ICLR, 2024)

Noisy Neighbors: Efficient membership inference attacks against LLMs. - Filippo Galli; Luca Melis; Tommaso Cucinotta (CoRR, 2024)

Auditing f-Differential Privacy in One Run. - Saeed Mahloujifar; Luca Melis; Kamalika Chaudhuri (CoRR, 2024)