# An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data

arXiv: https://arxiv.org/abs/2410.00069 · HTML: https://mlsanity.com/paper/2410.00069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Energy cost and machine learning accuracy impact of k-anonymisation and synthetic data techniques](https://mlsanity.com/paper/2305.07116/) (2023)

## Abstract (arXiv)

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.