A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data
No Thumbnail Available
Links to Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2019-08-21
Type of Work
Department
Program
Citation of Original Publication
Parameshwarappa, 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
Rights
This 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.
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.