Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity

TL;DR

This paper introduces a human-guided interactive anonymization method for privacy-preserving machine learning, allowing domain experts to influence data generalization to improve utility while maintaining privacy.

Contribution

It presents a novel interactive k-anonymization approach that incorporates human input to better preserve data utility compared to traditional automated methods.

Findings

Human input can improve data utility in anonymization.

Results vary depending on tasks and settings.

Interactive approach offers potential for better privacy-utility balance.

Abstract

Privacy-preserving machine learning (ML) seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or suppression of data attributes. In this study, we propose an interactive approach that incorporates human input into the k-anonymization process, enabling domain experts to guide attribute preservation based on contextual importance. Using the UCI Adult dataset, we compare classification outcomes of interactive human-influenced anonymization with traditional, fully automated methods. Our results show that human input can enhance data utility in some cases, although results vary across tasks and settings. We discuss limitations of our approach and suggest potential areas for improved interactive frameworks in privacy-aware ML.