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
188 2020 Diffprivlib: The IBM differential privacy library N Holohan, S Braghin, P Mac Aonghusa, K Levacher
arXiv preprint arXiv:1907.02444, 2019
169 * 2019 The bounded Laplace mechanism in differential privacy N Holohan, S Antonatos, S Braghin, P Mac Aonghusa
arXiv preprint arXiv:1808.10410, 2018
96 2018 Optimal differentially private mechanisms for randomised response N Holohan, DJ Leith, O Mason
IEEE Transactions on Information Forensics and Security 12 (11), 2726-2735, 2017
86 2017 ( N Holohan, S Antonatos, S Braghin, P Mac Aonghusa
arXiv preprint arXiv:1710.01615, 2017
34 * 2017 Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof N Holohan, DJ Leith, O Mason
Information Sciences 305, 256-268, 2015
34 2015 Extreme points of the local differential privacy polytope N Holohan, DJ Leith, O Mason
Linear Algebra and its Applications 534, 78-96, 2017
22 2017 Secure random sampling in differential privacy N Holohan, S Braghin
European Symposium on Research in Computer Security, 523-542, 2021
18 2021 Adaptive anonymization of data using statistical inference A Pascale, N Holohan, P Tommasi, S Deparis
US Patent App. 16/127,694, 2020
18 2020 Prima: an end-to-end framework for privacy at scale S Antonatos, S Braghin, N Holohan, Y Gkoufas, P Mac Aonghusa
2018 IEEE 34th international conference on data engineering (ICDE), 1531-1542, 2018
15 2018 Sensitive data policy recommendation based on compliance obligations of a data source S Antonatos, S Braghin, N Holohan, K Levacher, R Nair, M Stephenson
US Patent 11,562,087, 2023
12 2023 Watermarking anonymized datasets by adding decoys S Antonatos, S Braghin, N Holohan, P MacAonghusa
US Patent 10,997,279, 2021
10 2021 Applying a differential privacy operation on a cluster of data S Antonatos, S Braghin, N Holohan, P Mac Aonghusa
US Patent 10,769,306, 2020
8 2020 Federated Continual Learning with Differentially Private Data Sharing G Zizzo, A Rawat, N Holohan, S Tirupathi
Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022
7 2022 Detecting unauthorized use of sensitive information in content communicated over a network S Antonatos, S Braghin, N Holohan, P Mac Aonghusa
US Patent App. 15/882,583, 2019
6 2019 Mathematical Foundations of Differential Privacy N Holohan
Trinity College Dublin, 2017
6 2017 Providing consistent data masking using causal ordering S Antonatos, S Braghin, N Holohan, P MacAonghusa
US Patent 11,200,218, 2021
5 2021 Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection SR Kadhe, H Ludwig, N Baracaldo, A King, Y Zhou, K Houck, A Rawat, ...
arXiv preprint arXiv:2310.19304, 2023
4 2023 Random number generators and seeding for differential privacy N Holohan
arXiv preprint arXiv:2307.03543, 2023
4 2023 Fast linking of anonymized datasets S Antonatos, S Braghin, N Holohan, P MacAonghusa
US Patent 11,132,386, 2021
4 2021 
Stefano BraghinIBM ResearchEmail yang diverifikasi di ie.ibm.com