A5072342084- Cryptography and Data Security
- Complexity and Algorithms in Graphs
- Coding theory and cryptography
- Privacy-Preserving Technologies in Data
- Cryptographic Implementations and Security
- Cryptography and Residue Arithmetic
- Adversarial Robustness in Machine Learning
- Genomic variations and chromosomal abnormalities
- Cancer Genomics and Diagnostics
- Internet Traffic Analysis and Secure E-voting
- Security and Verification in Computing
- Oral and gingival health research
- Cloud Data Security Solutions
- Chaos-based Image/Signal Encryption
- Advanced Image and Video Retrieval Techniques
- Forensic and Genetic Research
- Embedded Systems Design Techniques
- Automated Road and Building Extraction
- Numerical Methods and Algorithms
- Blockchain Technology Applications and Security
- Parallel Computing and Optimization Techniques
- BIM and Construction Integration
- Forensic Toxicology and Drug Analysis
- Stochastic Gradient Optimization Techniques
- Digital Filter Design and Implementation
Zone Atelier Moselle
2021-2023
KU Leuven
2018-2021
Zama (France)
2021
IMEC
2018-2021
Laboratoire de Mathématiques de Versailles
2019
Laboratoire de Mathématiques
2016-2019
Imec the Netherlands
2019
Université Paris-Saclay
2016-2018
Université de Versailles Saint-Quentin-en-Yvelines
2016-2018
Centre National de la Recherche Scientifique
2016-2018
Fully Homomorphic Encryption (FHE) is a cryptographic primitive that allows performing arbitrary operations on encrypted data. Since the conception of idea in [RAD78], it has been considered holy grail cryptography. After first construction 2009 [Gen09], evolved to become practical with strong security guarantees. Most modern constructions are based well-known lattice problems such as Learning With Errors (LWE). Besides its academic appeal, recent years FHE also attracted significant...
Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic sharing with an untrusted service. Here, we developed secure efficient homomorphic encryption (HE) techniques. In HE-based methods, while it transit, at rest, and analysis. It only be decrypted by owner. We compared three state-of-the-art...
Floating-point arithmetic plays a central role in computer science and is used various domains where precision computational scale are essential. One notable application machine learning, Fully Homomorphic Encryption (FHE) can play crucial safeguarding user privacy. In this paper, we focus on TFHE develop novel homomorphic operators designed to enable the construction of precise adaptable floating-point operations. Integrating within context FHE particularly challenging due constraints such...
Oblivious RAM (ORAM) is a cryptographic primitive that allows client to hide access pattern its data encrypted and stored at remote server. Traditionally, ORAM algorithms assume the server acts purely as storage device. Under this assumption, has least log(N) bandwidth blowup for N entries. After three decades of improvements, have reached optimal logarithmic blowup. Nonetheless, in many practical use-cases constant overhead desirable. To purpose, Devadas et al. (TCC 2016) formalized...
ABSTRACT Genotype imputation is a fundamental step in genomic data analysis such as GWAS, where missing variant genotypes are predicted using the existing of nearby ‘tag’ variants. Imputation greatly decreases genotyping cost and provides high-quality estimates common genotypes. As population panels increase, e.g., TOPMED Project, genotype becoming more accurate, but it requires high computational power. Although researchers can outsource imputation, privacy concerns may prohibit genetic...
The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text images. In these services, client sends query to cloud server receives response in which case are revealed service provider. Such data disclosures unacceptable scenarios due sensitivity and/or privacy laws. this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's result confidential. SANNS comprises two...