# Energy cost and machine learning accuracy impact of k-anonymisation and synthetic data techniques

arXiv: https://arxiv.org/abs/2305.07116 · HTML: https://mlsanity.com/paper/2305.07116/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers](https://mlsanity.com/paper/2102.04763/) (2021)
- [A Statistical Approach to Adult Census Income Level Prediction](https://mlsanity.com/paper/1810.10076/) (2018)
- [Privacy attitudes and concerns in the digital lives of older adults: Westin's privacy attitude typology revisited](https://mlsanity.com/paper/1801.05047/) (2018)

## Built on by (most cited first, 2 of 2)

- [Multi-Objective Optimization-Based Anonymization of Structured Data for Machine Learning Application](https://mlsanity.com/paper/2501.01002/) (2025)
- [An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data](https://mlsanity.com/paper/2410.00069/) (2024)

## Abstract (arXiv)

To address increasing societal concerns regarding privacy and climate, the EU adopted the General Data Protection Regulation (GDPR) and committed to the Green Deal. Considerable research studied the energy efficiency of software and the accuracy of machine learning models trained on anonymised data sets. Recent work began exploring the impact of privacy-enhancing techniques (PET) on both the energy consumption and accuracy of the machine learning models, focusing on k-anonymity. As synthetic data is becoming an increasingly popular PET, this paper analyses the energy consumption and accuracy of two phases: a) applying privacy-enhancing techniques to the concerned data set, b) training the models on the concerned privacy-enhanced data set. We use two privacy-enhancing techniques: k-anonymisation (using generalisation and suppression) and synthetic data, and three machine-learning models. Each model is trained on each privacy-enhanced data set. Our results show that models trained on k-anonymised data consume less energy than models trained on the original data, with a similar performance regarding accuracy. Models trained on synthetic data have a similar energy consumption and a similar to lower accuracy compared to models trained on the original data.