Paper 2019/101

Privacy-preserving semi-parallel logistic regression training with Fully Homomorphic Encryption

Sergiu Carpov, Nicolas Gama, Mariya Georgieva, and Juan Ramon Troncoso-Pastoriza


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. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last several years. However, it is still challenging to apply them on large biomedical datasets. Methods The HE Track of iDash 2018 competition focused on solving an important problem in practical machine learning scenarios, where a data analyst that has trained a regression model (both linear and logistic) with a certain set of features, attempts to find all features in an encrypted database that will improve the quality of the model. Our solution is based on the hybrid framework Chimera that allows for switching between different families of fully homomorphic schemes, namely TFHE and HEAAN. Results Our solution is one of the finalist of Track 2 of iDash 2018 competition. Among the submitted solutions, ours is the only bootstrapped approach that can be applied for different sets of parameters without re-encrypting the genomic database, making it practical for real-world applications. Conclusions This is the first step towards the more general feature selection problem across large encrypted databases.

Available format(s)
Publication info
Preprint. MINOR revision.
fully homomorphic encryptionlogistic regressiongenome privacygenome-wide association study
Contact author(s)
sergiu carpov @ cea fr
juan troncoso-pastoriza @ epfl ch
maria georgievabs @ gmail com
nicolas gama @ gmail com
2019-01-31: received
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Creative Commons Attribution


      author = {Sergiu Carpov and Nicolas Gama and Mariya Georgieva and Juan Ramon Troncoso-Pastoriza},
      title = {Privacy-preserving semi-parallel logistic regression training with Fully Homomorphic Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2019/101},
      year = {2019},
      note = {\url{}},
      url = {}
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