  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.
