A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference [PDF]
Scientific ReportsMedical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring.
Sadhana Selvakumar, B. Senthilkumar
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Efficient TFHE Bootstrapping in the Multiparty Setting
IEEE Access, 2023 TFHE is a practical fully homomorphic encryption scheme (FHE) capable of computing any boolean gate or non-linear function. The scheme was originally designed to work for the single key setting.
Jeongeun Park, Sergi Rovira
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Non-Interactive Decision Trees and Applications with Multi-Bit TFHE [PDF]
Algorithms, 2022 Machine learning classification algorithms, such as decision trees and random forests, are commonly used in many applications. Clients who want to classify their data send them to a server that performs their inference using a trained model.
Jestine Paul +3 more
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Improved homomorphic evaluation for hash function based on TFHE [PDF]
CybersecurityHomomorphic evaluation of hash functions offers a solution to the challenge of data integrity authentication in the context of homomorphic encryption. The earliest attempt to achieve homomorphic evaluation of SHA-256 hash function was proposed by Mella ...
Benqiang Wei, Xianhui Lu
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Sharing the Mask: TFHE Bootstrapping on Packed Messages
Transactions on Cryptographic Hardware and Embedded SystemsFully Homomorphic Encryption (FHE) schemes typically experience significant data expansion during encryption, leading to increased computational costs and memory demands during homomorphic evaluations compared to their plaintext counterparts.
Loris Bergerat +5 more
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Revisiting the functional bootstrap in TFHE
Transactions on Cryptographic Hardware and Embedded Systems, 2021 The FHEW cryptosystem introduced the idea that an arbitrary function can be evaluated within the bootstrap procedure as a table lookup. The faster bootstraps of TFHE strengthened this approach, which was later named Functional Bootstrap (Boura et al ...
Antonio Guimarães +2 more
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Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption [PDF]
BMC Medical Genomics, 2020 Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population.
Sergiu Carpov +3 more
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VeloFHE: GPU Acceleration for FHEW and TFHE Bootstrapping
Transactions on Cryptographic Hardware and Embedded SystemsBit-wise Fully Homomorphic Encryption schemes like FHEW and TFHE offer efficient functional bootstrapping, enabling concurrent function evaluation and noise reduction.
Shiyu Shen +8 more
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DMAFL: Effective defense against malicious attacker federated learning framework via blockchain and TFHE [PDF]
Journal of King Saud University: Computer and Information SciencesA blockchain and threshold fully homomorphic encryption (TFHE)-based federated learning framework is proposed to defend against model poisoning and privacy leakage.
Chunhua Jin +6 more
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GPU Acceleration for FHEW/TFHE Bootstrapping
Transactions on Cryptographic Hardware and Embedded SystemsFully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without decryption. Despite its great theoretical potential, the computational overhead remains a major obstacle for practical applications.
Yu Xiao +7 more
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