6 citations · 7 across the 3 of their papers we have counts for
3 papers
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Itamar Zimerman, Moran Baruch, Nir Drucker +3
Designing privacy-preserving deep learning models is a major challenge within the deep learning community. Homomorphic Encryption (HE) has emerged as one of the most promising appr…
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…
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…