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.

翻译：数字时代带来了诸多社会挑战，包括信息通信技术日益增长的能源消耗以及个人数据处理中的隐私保护问题。本文通过跨学科视角，探讨这两个方面与机器学习准确性之间的关联。我们首先提出一种量化隐私增强技术对数据效用与能耗影响的方法，并通过实验装置揭示了环境-隐私-准确性的权衡关系。随后采用叙事性研究方法，将这些技术发现转化为非信息通信技术领域专家可理解的表述。我们以政府审计场景为例构建了两个实例，将研究结果置于具体语境中阐释。最终，用户需要在能耗、隐私与准确性之间权衡优化其数据处理操作，而决策的影响效果具有显著的情境依赖性。