An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

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 article 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.

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

“…In anonymization, the clustering process usually adopts some anonymity requirements as well (i.e., k users in a cluster/class); hence, computation complexity increases drastically [253]. Although some efforts have been devoted to lowering the computing complexity in CAMs [254], [255], reducing the computing burdens of CAMs on high-dimensional and large datasets is still very challenging.…”