4 papers · 1 filter
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca +2
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditiona…
Differentially Private Hierarchical Heavy Hitters
Ari Biswas, Graham Cormode, Yaron Kanza +2
The task of finding _Hierarchical_ Heavy Hitters (HHH) was introduced by Cormode et al. [VLDB 2003] as a generalisation of the heavy hitter problem. While finding HHH in data strea…
Privacy-preserving Fuzzy Name Matching for Sharing Financial Intelligence
Harsh Kasyap, Ugur Ilker Atmaca, Carsten Maple +2
Financial institutions rely on data for many operations, including a need to drive efficiency, enhance services and prevent financial crime. Data sharing across an organisation or…
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
Samuel Maddock, Graham Cormode, Carsten Maple
Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirr…