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;48(12):3791 View
5. Senanayake N, Podschwadt R, Takabi D, Calhoun V, Plis S. NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data. Neuroinformatics 2022;20(1):91 View
6. Chen R, Zhang Y, Dou Z, Chen F, Xie K, Wang S. Data Sharing and Privacy in Pharmaceutical Studies. Current Pharmaceutical Design 2021;27(7):911 View
7. Wirth F, Kussel T, Müller A, Hamacher K, Prasser F. EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation. BMC Bioinformatics 2022;23(1) View
8. Zhang L, Vashisht H, Totev A, Trinh N, Ward T. A comparison of distributed machine learning methods for the support of “many labs” collaborations in computational modeling of decision making. Frontiers in Psychology 2022;13 View
9. Dankar F, Gergely M, Dankar S. Informed Consent in Biomedical Research. Computational and Structural Biotechnology Journal 2019;17:463 View
10. Torkzadehmahani R, Nasirigerdeh R, Blumenthal D, Kacprowski T, List M, Matschinske J, Spaeth J, Wenke N, Baumbach J. Privacy-Preserving Artificial Intelligence Techniques in Biomedicine. Methods of Information in Medicine 2022;61(S 01):e12 View
11. Spini G, Mancini E, Attema T, Abspoel M, de Gier J, Fehr S, Veugen T, van Heesch M, Worm D, De Luca A, Cramer R, Sloot P. New Approach to Privacy-Preserving Clinical Decision Support Systems for HIV Treatment. Journal of Medical Systems 2022;46(12) View
12. Brauneck A, Schmalhorst L, Kazemi Majdabadi M, Bakhtiari M, Völker U, Saak C, Baumbach J, Baumbach L, Buchholtz G. Federated machine learning in data-protection-compliant research. Nature Machine Intelligence 2023;5(1):2 View
13. Sathish Kumar G, Premalatha K, Uma Maheshwari G, Rajesh Kanna P, Vijaya G, Nivaashini M. Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation. Engineering Applications of Artificial Intelligence 2024;128:107399 View

Books/Policy Documents

1. Dankar F, Madathil N. Advances in Smart Technologies Applications and Case Studies. View
2. Alkhozae M, Zeng X. Advances in Computational Intelligence Systems. View
3. Alkhozae M, Zeng X. Advances in Computational Intelligence Systems. View
4. Hristov-Kalamov N, Fernández-Ruiz R, álvarez-Marquina A, Núñez-Vidal E, Domínguez-Mateos F, Palacios-Alonso D. Artificial Intelligence for Neuroscience and Emotional Systems. View
5. Eklund D, Iacovazzi A, Wang H, Pyrgelis A, Raza S. Computer Security – ESORICS 2024. View