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Deep Neural Network models require significant energy resources, increasing the need to balance performance with sustainability, a field known as Green AI. This paper investigates the intersection of accuracy, privacy, and energy efficiency within Deep Learning-based, Privacy-Preserving Record Linkage. Through a series of experiments, the effects of key parameters on matching efficiency and energy consumption are explored, outlining the impact of encoding, noise addition, and deep learning model configurations. The findings indicate clear trade-offs between energy consumption, privacy, and matching performance.

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An Evaluation of Energy Consumption for Deep Learning-Based Privacy Preserving Record Linkage

  • 会议论文

  • 发表日期：

    2026年

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  • Emmanouil Sokorelis,
  • Alexandros Karakasidis,
  • Eftychios Protopapadakis,
  • Chairi Kiourt

摘要

Deep Neural Network models require significant energy resources, increasing the need to balance performance with sustainability, a field known as Green AI. This paper investigates the intersection of accuracy, privacy, and energy efficiency within Deep Learning-based, Privacy-Preserving Record Linkage. Through a series of experiments, the effects of key parameters on matching efficiency and energy consumption are explored, outlining the impact of encoding, noise addition, and deep learning model configurations. The findings indicate clear trade-offs between energy consumption, privacy, and matching performance.