51 citations · 86 across the 11 of their papers we have counts for
11 papers
Efficient Decoding Methods for Language Models on Encrypted Data
Matan Avitan, Moran Baruch, Nir Drucker +2
Large language models (LLMs) power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption (HE) enables computati…
Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Itamar Zimerman, Allon Adir, Ehud Aharoni +7
Modern cryptographic methods for implementing privacy-preserving LLMs such as \gls{HE} require the LLMs to have a polynomial form. Forming such a representation is challenging beca…
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…
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…
Efficient Skip Connections Realization for Secure Inference on Encrypted Data
Nir Drucker, Itamar Zimerman
Homomorphic Encryption (HE) is a cryptographic tool that allows performing computation under encryption, which is used by many privacy-preserving machine learning solutions, for ex…
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…
Efficient Pruning for Machine Learning Under Homomorphic Encryption
Ehud Aharoni, Moran Baruch, Pradip Bose +8
Privacy-preserving machine learning (PPML) solutions are gaining widespread popularity. Among these, many rely on homomorphic encryption (HE) that offers confidentiality of the mod…
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…
A methodology for training homomorphicencryption friendly neural networks
Moran Baruch, Nir Drucker, Lev Greenberg +1
Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance and retail. Recently, homomorphic encryption (HE…
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…