Jean-Pierre Hubaux

Jean-Pierre Hubaux is a full professor at EPFL and head of the Laboratory for Data Security. Through his research, he contributes to laying the foundations and developing the tools for protecting privacy in today’s hyper-connected world. He has pioneered the areas of privacy and security in mobile/wireless networks and in personalized health. He is the academic director of the Center for Digital Trust (C4DT). He leads the Data Protection in Personalized Health (DPPH) project funded by the ETH Council and is a co-chair of the Data Security Work Stream of the Global Alliance for Genomics and Health (GA4GH). From 2008 to 2019 he was one of the seven commissioners of the Swiss FCC. He is a Fellow of both IEEE (2008) and ACM (2010). Recent awards: two of his papers obtained distinctions at the IEEE Symposium on Security and Privacy in 2015 and 2018. He is among the most cited researchers in privacy protection and in information security. Spoken languages: French, English, German, Italian

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Privacy

Privacy (UK, US) is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. The domain of privacy partially overlaps

Wireless ad hoc network

A wireless ad hoc network (WANET) or mobile ad hoc network (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such

Game theory

Game theory is the study of mathematical models of strategic interactions among rational agents. It has applications in all fields of social science, as well as in logic, systems science and computer

Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation

Jean-Philippe Léonard Bossuat, Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza

Bootstrapping parameters for the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17), are usually instantiated using sparse secrets to be efficient. However, using sparse secrets constrains the range of practical parameters within a tight interval, as they must support a large enough depth for the bootstrapping circuit but also be secure with respect to the sparsity of their secret. We present a bootstrapping procedure for the CKKS scheme that combines both dense and sparse secrets. Our construction enables the use of parameters for which the homomorphic capacity is based on a dense secret, yet with a bootstrapping complexity that remains the one of a sparse secret and with a large security margin. Moreover, this also enables us to easily parameterize the bootstrapping circuit so that it has a negligible failure probability that, to the best of our knowledge, has never been achieved for the CKKS scheme. When using the parameters of previous works, our bootstrapping procedures enable a faster execution with an increased precision and lower failure probability. For example, we are able to bootstrap a plaintext of C-32768 in 20.2 s, with 32.11 bits of precision, 285 remaining modulus bits, a failure probability of 2(-138.7), and 128 bit security.

SPRINGER INTERNATIONAL PUBLISHING AG2022

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Erman Ayday, Jean-Pierre Hubaux, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza

2022

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

Jeffrey Chen, David Jules Froelicher, Jean-Pierre Hubaux, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza

Sharing data across multiple institutions for genome-wide association studies (GWAS) would enable discovery of novel genetic variants linked to health and disease. However, existing regulations on genomic data sharing and the sheer size of the data limit the scope of such collaborations. Although cryptographic tools for secure computation promise to enable collaborative studies with formal privacy guarantees, existing approaches either are computationally impractical or support only simplified analysis pipelines. Here, we introduce secure and federated genome-wide association studies (SF-GWAS), a novel combination of secure computation frameworks that empowers efficient and accurate GWAS in a federated manner, i.e., on private data locally-held by multiple entities, while provably ensuring end-to-end data confidentiality. Another key advance is that we designed SF-GWAS to support the two most widely-used GWAS pipelines—those based on principal component analysis (PCA) or linear mixed models (LMMs). We ran SF-GWAS on five real GWAS datasets, including a large UK Biobank cohort of 410K individuals, thereby demonstrating the largest secure genetics collaboration to date. SF-GWAS achieves an order-of-magnitude runtime improvement over the prior art for PCA-based GWAS and newly allows secure LMM-based association tests, for which its runtime scales at a near-constant rate in cohort size. Our work realizes the power of secure, collaborative, and accurate GWAS at unprecedented scale and should be applicable to a broad range of analyses. Our open-source software is at: https://github.com/hhcho/sfgwas.

2022