Jean-Philipe Bossuat
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    • TETRIS: Composing FHE Techniques for Private Functional Exploration Over Large Datasets
    • Practical q-IND-CPA-D-Secure Approximate Homomorphic Encryption
    • Security Guidelines for Implementing Homomorphic Encryption
    • Scalable and Privacy-Preserving Federated Principal Component Analysis
    • Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing
    • Privacy-Preserving Federated Recurrent Neural Networks
    • Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation
    • POSEIDON: Privacy-Preserving Federated Neural Network Learning
    • Privacy-preserving federated neural network learning for disease-associated cell classification
    • slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference
    • Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys
    • Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
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    • Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation
    • Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
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IDASH 2024

Nov 9, 2024 · 0 min read
Abstract
Presentation of the Track 1. Secure Neural Network Evaluation for Protein Sequence Classification winning solution.
Date
Nov 9, 2024
Event
IDASH PRIVACY & SECURITY WORKSHOP 2024 - Secure Genome Analysis Competition
Location

Zoom

Last updated on Apr 3, 2025
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