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In the field of Data Science, Data Governance is an important aspect. With the promulgation of the GDPR (General Data Protection Regulation), data privacy and security concerns are regularised, and researchers are seeking attention to solve the number of associated issues and challenges. The data may be disclosed at various stages of ML model construction, such as data collection, model training, or even after the trained model is released on the market. To solve this, the Fog-based Federated Learning aspect supports collaborative learning in which ML models are constructed locally, and only gradient/ update parameters are shared with the global server. However, the attacks are still possible if the gradients are disclosed. The differential privacy aspect of privacy preservation can be used to protect sensitive data against those attacks. In this article, we propose a differential privacy preservation enabled by the Fog-based Federated Learning framework to ensure users’ privacy for health care data from renowned attacks such as model inversion and data reconstruction attacks. Validation of the proposed framework is done using the iFogsim simulator.

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Handling Sensitive Medical Data—A Differential Privacy Enabled Federated Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • T. Suriya Praba,
  • S. Reka,
  • V. Meena,
  • Bharath Reddy Gudibandi,
  • Sai Surya Akella,
  • Jaya Nikhita Gudiseva

摘要

In the field of Data Science, Data Governance is an important aspect. With the promulgation of the GDPR (General Data Protection Regulation), data privacy and security concerns are regularised, and researchers are seeking attention to solve the number of associated issues and challenges. The data may be disclosed at various stages of ML model construction, such as data collection, model training, or even after the trained model is released on the market. To solve this, the Fog-based Federated Learning aspect supports collaborative learning in which ML models are constructed locally, and only gradient/ update parameters are shared with the global server. However, the attacks are still possible if the gradients are disclosed. The differential privacy aspect of privacy preservation can be used to protect sensitive data against those attacks. In this article, we propose a differential privacy preservation enabled by the Fog-based Federated Learning framework to ensure users’ privacy for health care data from renowned attacks such as model inversion and data reconstruction attacks. Validation of the proposed framework is done using the iFogsim simulator.