Automated Privacy-Preserving Techniques via Meta-Learning

TL;DR

This paper introduces AUTOPRIV, an automated meta-learning-based method that simplifies privacy-preserving data sharing for machine learning by eliminating manual configuration and predicting optimal privacy solutions.

Contribution

AUTOPRIV is the first fully automated privacy-preservation technique using meta-learning, reducing complexity and enabling secure data sharing without expert intervention.

Findings

AUTOPRIV effectively predicts privacy configurations with high accuracy.

It significantly reduces computational complexity and energy consumption.

The method achieves near-optimal privacy-performance trade-offs.

Abstract

Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aiming to transform the data while ensuring the privacy of individuals. Some of these techniques have been incorporated into tools, whereas others are accessed through various online platforms. However, such tools require manual configuration, which can be complex and time-consuming. Moreover, they require substantial expertise, potentially restricting their use to those with advanced technical knowledge. In this paper, we propose AUTOPRIV, the first automated privacy-preservation method, that eliminates the need for any manual configuration. AUTOPRIV employs meta-learning to automate the de-identification process, facilitating the secure release of data for machine learning tasks. The main goal is to anticipate the predictive performance and privacy risk of a large set of privacy configurations. We provide a ranked list of the most promising solutions, which are likely to achieve an optimal approximation within a new domain. AUTOPRIV is highly effective as it reduces computational complexity and energy consumption considerably.