A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data

TL;DR

This paper introduces a multi-level clustering anonymization method for large-scale physical activity data, significantly reducing computational costs while maintaining data utility for health research.

Contribution

The paper proposes a novel multi-level clustering approach that improves efficiency in anonymizing sequential physical activity data compared to traditional methods.

Findings

Reduces clustering time drastically

Maintains data utility comparable to conventional methods

Effective for large-scale sequential data anonymization

Abstract

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This paper presents an effective anonymization approach, Multi-level Clustering based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.