Energy cost and machine learning accuracy impact of k-anonymisation and synthetic data techniques
It addresses energy and privacy trade-offs for machine learning practitioners, but is incremental as it extends prior work on k-anonymity to include synthetic data.
This paper tackles the problem of evaluating the energy consumption and accuracy impacts of privacy-enhancing techniques (k-anonymisation and synthetic data) on machine learning models, finding that models trained on k-anonymised data consume less energy with similar accuracy, while those on synthetic data have similar energy but similar to lower accuracy compared to original data.
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.