78 citations · 151 across the 6 of their papers we have counts for
6 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…
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…
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data
Ehud Aharoni, Allon Adir, Moran Baruch +9
Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage…
Data Minimization for GDPR Compliance in Machine Learning Models
Abigail Goldsteen, Gilad Ezov, Ron Shmelkin +2
The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. How…
Anonymizing Machine Learning Models
Abigail Goldsteen, Gilad Ezov, Ron Shmelkin +2
There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protectio…
Reducing Risk of Model Inversion Using Privacy-Guided Training
Abigail Goldsteen, Gilad Ezov, Ariel Farkash
Machine learning models often pose a threat to the privacy of individuals whose data is part of the training set. Several recent attacks have been able to infer sensitive informati…