研究数据处理中能耗、隐私与准确率的权衡关系。
An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data
- 提出量化隐私技术对数据效用和能耗影响的方法。
- 实验发现三者存在环境-隐私-准确率的权衡关系。
- 以故事化方式向非技术专家传达结果，适合政策制定者。
数字时代带来了诸多社会挑战，包括信息技术（ICT）能耗上升以及个人数据处理中的隐私保护问题。本文从跨学科视角探讨机器学习准确性与上述两方面的权衡。首先提出一种方法，用于衡量隐私增强技术对数据效用和能耗的影响；通过实验揭示了环境、隐私与准确率之间的权衡关系。随后采用叙事方法，将技术发现转化为非信息通信技术领域专家可理解的形式，分别以政府和审计场景为例进行情境化阐释。最终指出，用户需在能耗、隐私与准确率之间权衡决策，且其影响具有高度情境依赖性。
原文摘要 · Abstract (English)
The digital era has raised many societal challenges, including ICT's rising energy consumption and protecting privacy of personal data processing. This paper considers both aspects in relation to machine learning accuracy in an interdisciplinary exploration. We first present a method to measure the effects of privacy-enhancing techniques on data utility and energy consumption. The environmental-privacy-accuracy trade-offs are discovered through an experimental set-up. We subsequently take a storytelling approach to translate these technical findings to experts in non-ICT fields. We draft two examples for a governmental and auditing setting to contextualise our results. Ultimately, users face the task of optimising their data processing operations in a trade-off between energy, privacy, and accuracy considerations where the impact of their decisions is context-sensitive.
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