112 citations · 239 across the 9 of their papers we have counts for
13 papers
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo +12
The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and…
Random Number Generators and Seeding for Differential Privacy
Naoise Holohan
Differential Privacy (DP) relies on random numbers to preserve privacy, typically utilising Pseudorandom Number Generators (PRNGs) as a source of randomness. In order to allow for…
Secure k-Anonymization over Encrypted Databases
Manish Kesarwani, Akshar Kaul, Stefano Braghin +2
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important s…
Secure Random Sampling in Differential Privacy
Naoise Holohan, Stefano Braghin
Differential privacy is among the most prominent techniques for preserving privacy of sensitive data, oweing to its robust mathematical guarantees and general applicability to a va…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Diffprivlib: The IBM Differential Privacy Library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa +1
Since its conception in 2006, differential privacy has emerged as the de-facto standard in data privacy, owing to its robust mathematical guarantees, generalised applicability and…