A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data
This work addresses privacy risks in publishing health data for researchers, but it is incremental as it improves efficiency over conventional methods without a major paradigm shift.
The paper tackles the problem of anonymizing sequential physical activity data, which is computationally expensive with existing methods, by proposing a multi-level clustering approach that drastically reduces clustering time while preserving data utility.
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.