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AI in Healthcare Data Privacy-Preserving: Enhanced Trade-Off Between Security and Utility

  • Lian Peng,
  • Meikang Qiu

摘要

The digital shift in healthcare has spurred progress in medical services. However, this progress has introduced substantial security risks, necessitating a balance between data privacy and utility. This paper examines the challenges of managing healthcare data privacy, focusing on the trade-offs between various challenges. Then we discuss the application of AI in healthcare privacy data protection, highlighting its role in various scenarios. The paper proposes a novel approach for healthcare data privacy-preserving. The most essential challenge of privacy-preserving is a trade-off between privacy and utility. AI can enhance the efficacy of privacy-preserving measures to mitigate the competitive intensity of the trade-off process. Then we validate this perspective with a systematic analysis of five typical scenarios in healthcare data privacy-preserving, respectively.