A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data
Links to Fileshttps://arxiv.org/abs/1908.07976
MetadataShow full item record
Type of Work24 pages
journal articles preprints
Citation of Original PublicationParameshwarappa, Pooja; Chen, Zhiyuan; Koru, Güneṣ; A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data; Cryptography and Security; https://arxiv.org/abs/1908.07976
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
health-related longitudinal data
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.