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Comparison of an Accelerated Garble Embedding Methodology for Privacy Preserving in Biomedical Data Analytics

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Artificial Intelligence for Neuroscience and Emotional Systems (IWINAC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14674))

Abstract

This research work proposes a novel, encryption-based method for comparing embeddings generated by neural networks on various information types (text, images, videos, audio, etc.). This approach prioritizes real-world applications dealing with sensitive or private data, particularly in biomedical and biometric analysis, where even minor information leaks can be highly detrimental. To address this concern, the method performs all necessary calculations within a highly secure and efficient encryption layer. Notably, this work introduces practical solutions applicable to real-world biomedical data scenarios.

This work was supported in part by Spanish Ministerio de Ciencia e Innovación under Grant PID2021-124176OB-I00, in part by Universidad Rey Juan Carlos, and in part by the Spanish General Directorate of Police.

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Authors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Tulipán S/N, Móstoles, 28933, Spain

    Nikola Hristov-Kalamov, Raúl Fernández-Ruiz, Esther Núñez-Vidal, Francisco Domínguez-Mateos & Daniel Palacios-Alonso

  2. Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, 28220, Spain

    Agustín álvarez-Marquina & Daniel Palacios-Alonso

Authors
  1. Nikola Hristov-Kalamov
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  2. Raúl Fernández-Ruiz
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  3. Agustín álvarez-Marquina
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  4. Esther Núñez-Vidal
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  5. Francisco Domínguez-Mateos
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  6. Daniel Palacios-Alonso
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Correspondence to Daniel Palacios-Alonso .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Polytechnic University of Valencia, Valencia, Spain

    Mikel Val Calvo

  3. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Hristov-Kalamov, N., Fernández-Ruiz, R., álvarez-Marquina, A., Núñez-Vidal, E., Domínguez-Mateos, F., Palacios-Alonso, D. (2024). Comparison of an Accelerated Garble Embedding Methodology for Privacy Preserving in Biomedical Data Analytics. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-61140-7_28

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