Published on in Vol 7 , No 2 (2019) :Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12702, first published .
Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Journals

  1. Eicher J, Bild R, Spengler H, Kuhn K, Prasser F. A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models. BMC Medical Informatics and Decision Making 2020;20(1) View
  2. Rankin D, Black M, Bond R, Wallace J, Mulvenna M, Epelde G. Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. JMIR Medical Informatics 2020;8(7):e18910 View
  3. Dankar F. Data privacy through participant empowerment. Nature Computational Science 2021;1(3):175 View
  4. Kirienko M, Sollini M, Ninatti G, Loiacono D, Giacomello E, Gozzi N, Amigoni F, Mainardi L, Lanzi P, Chiti A. Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. European Journal of Nuclear Medicine and Molecular Imaging 2021 View
  5. Senanayake N, Podschwadt R, Takabi D, Calhoun V, Plis S. NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data. Neuroinformatics 2021 View

Books/Policy Documents

  1. Dankar F, Madathil N. Advances in Smart Technologies Applications and Case Studies. View