10 citations · 14 across the 4 of their papers we have counts for
4 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…
Generating One-Hot Maps under Encryption
Ehud Aharoni, Nir Drucker, Eyal Kushnir +2
One-hot maps are commonly used in the AI domain. Unsurprisingly, they can also bring great benefits to ML-based algorithms such as decision trees that run under Homomorphic Encrypt…
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
Moran Baruch, Nir Drucker, Gilad Ezov +5
Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations. This limits HE-based…
Privacy-preserving record linkage using local sensitive hash and private set intersection
Allon Adir, Ehud Aharoni, Nir Drucker +4
The amount of data stored in data repositories increases every year. This makes it challenging to link records between different datasets across companies and even internally, whil…