大规模体力活动数据分析的多层次聚类方法
A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data
摘要：
发布身体活动数据可以促进人口健康管理、行为健康研究和慢性健康问题管理等多个领域的可重复性卫生保健研究。然而，发布这些数据也带来了与重新识别相关的高隐私风险，这使得匿名化成为必要。匿名化定期收集的身体活动数据的挑战之一是其连续性。现有的匿名化技术对横截面数据足够有效，但直接应用于序列数据时计算成本很高。本文提出了一种有效的匿名化方法，基于多级聚类的匿名化身体活动数据。与传统方法相比，该方法大大减少了聚类时间，从而提高了时间复杂度。在这样做的同时，它保留了与传统方法一样多的实用性。
英文摘要：
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.
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