Securing Floating-Point Arithmetic for Noise Addition
- 2024
- CCS 2024
Bio
Naoise Holohan is a research scientist in the Privacy and Security of AI team at the Dublin Research Lab. Since joining IBM in 2017, he has been the differential privacy lead in the team, contributing to the Data Privacy Toolkit and leading the development of the Differential Privacy Library, an open source machine learning library for differential privacy. Prior to joining IBM, Naoise graduated with a PhD from Trinity College Dublin for his work on "Mathematical Foundations of Differential Privacy".
Publications
Federated Continual Learning with Differentially Private Data Sharing
- 2022
- NeurIPS 2022
Secure Random Sampling in Differential Privacy
- 2021
- ESORICS 2021
Secure k-Anonymization over Encrypted Databases
- Manish Kesarwani
- Akshar Kaul
- et al.
- 2021
- CLOUD 2021
The bounded laplace mechanism in differential privacy
- Naoise Holohan
- Spiros Antonatos
- et al.
- 2020
- JPC
AnonTokens: tracing re-identification attacks through decoy records
- Spiros Antonatos
- Stefano Braghin
- et al.
- 2019
- arXiv
Prima: An end-to-end framework for privacy at scale
- Spiros Antonatos
- Stefano Braghin
- et al.
- 2018
- ICDE 2018
Extreme points of the local differential privacy polytope
- Naoise Holohan
- Douglas J. Leith
- et al.
- 2017
- Linear Algebra and Its Applications
Optimal Differentially Private Mechanisms for Randomised Response
- Naoise Holohan
- Douglas J. Leith
- et al.
- 2017
- IEEE TIFS
(k,ε)-Anonymity: k-Anonymity with ε-Differential Privacy
- Naoise Holohan
- Spiros Antonatos
- et al.
- 2017
- arXiv
Patents
- CN
- ZL202111352718.3
- US
- 11562087
- US
- 11392487
- US
- 11200218
- US
- 11132386
- US
- 10997279
- US
- 10769306
Projects
Blog posts
Top collaborators
SB
Stefano Braghin
Research Engineer and Technical Lead - Security and Privacy, Master Inventor
ST
AR
Ambrish Rawat
Senior Research Scientist, AI Security and Privacy, Master Inventor
GZ
Giulio Zizzo
Staff Research Scientist