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ORCIDNaoise Holohan
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2020 – today
- 2024
[c4]Naoise Holohan, Stefano Braghin, Mohamed Suliman:
Securing Floating-Point Arithmetic for Noise Addition. CCS 2024: 1954-1966- 2023
- [i12]Naoise Holohan:
Random Number Generators and Seeding for Differential Privacy. CoRR abs/2307.03543 (2023) - [i11]Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo, Alan King, Yi Zhou, Keith Houck, Ambrish Rawat, Mark Purcell, Naoise Holohan, Mikio Takeuchi, Ryo Kawahara, Nir Drucker, Hayim Shaul, Eyal Kushnir, Omri Soceanu:
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. CoRR abs/2310.19304 (2023) - 2021
- [c3]Manish Kesarwani, Akshar Kaul, Stefano Braghin
, Naoise Holohan, Spiros Antonatos:
Secure k-Anonymization over Encrypted Databases. CLOUD 2021: 20-30 - [c2]Naoise Holohan, Stefano Braghin:
Secure Random Sampling in Differential Privacy. ESORICS (2) 2021: 523-542 - [i10]Naoise Holohan, Stefano Braghin:
Secure Random Sampling in Differential Privacy. CoRR abs/2107.10138 (2021) - [i9]Manish Kesarwani, Akshar Kaul, Stefano Braghin, Naoise Holohan, Spiros Antonatos:
Secure k-Anonymization over Encrypted Databases. CoRR abs/2108.04780 (2021) - 2020
- [j4]Naoise Holohan, Spiros Antonatos, Stefano Braghin, Pól Mac Aonghusa:
The Bounded Laplace Mechanism in Differential Privacy. J. Priv. Confidentiality 10(1) (2020) - [i8]Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Jeremy Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Ngoc Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Witherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay:
IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR abs/2007.10987 (2020)
2010 – 2019
- 2019
- [i7]Spiros Antonatos, Stefano Braghin, Naoise Holohan, Pol Mac Aonghusa:
AnonTokens: tracing re-identification attacks through decoy records. CoRR abs/1906.09829 (2019) - [i6]Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, Killian Levacher:
Diffprivlib: The IBM Differential Privacy Library. CoRR abs/1907.02444 (2019) - 2018
- [c1]Spiros Antonatos, Stefano Braghin, Naoise Holohan, Yiannis Gkoufas, Pol Mac Aonghusa:
PRIMA: An End-to-End Framework for Privacy at Scale. ICDE 2018: 1531-1542 - [i5]Naoise Holohan, Spiros Antonatos, Stefano Braghin, Pol Mac Aonghusa:
The Bounded Laplace Mechanism in Differential Privacy. CoRR abs/1808.10410 (2018) - 2017
- [b1]Naoise Holohan:
Mathematical foundations of differential privacy. Trinity College Dublin, Ireland, 2017 - [j3]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Optimal Differentially Private Mechanisms for Randomised Response. IEEE Trans. Inf. Forensics Secur. 12(11): 2726-2735 (2017) - [i4]Naoise Holohan, Spiros Antonatos, Stefano Braghin, Pol Mac Aonghusa:
(k, ε)-Anonymity: k-Anonymity with ε-Differential Privacy. CoRR abs/1710.01615 (2017) - 2016
- [j2]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Differentially private response mechanisms on categorical data. Discret. Appl. Math. 211: 86-98 (2016) - [i3]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Optimal Differentially Private Mechanisms for Randomised Response. CoRR abs/1612.05568 (2016) - 2015
- [j1]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof. Inf. Sci. 305: 256-268 (2015) - [i2]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Differentially Private Response Mechanisms on Categorical Data. CoRR abs/1505.07254 (2015) - 2014
- [i1]Naoise Holohan, Douglas J. Leith, Oliver Mason:
Differential Privacy in Metric Spaces: Numerical, Categorical and Functional Data Under the One Roof. CoRR abs/1402.6124 (2014)
Coauthor Index
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