Privacy-preserving human activity sensing: A survey

Abstract

With the prevalence of various sensors and smart devices in people’s daily lives, numerous types of information are being sensed. While using such information provides critical and convenient services, we are gradually exposing every piece of our behavior and activities. Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities. This survey reviews existing studies on privacy-preserving human activity sensing. We first introduce the sensors and captured private information related to human activities. We then propose a taxonomy to structure the methods for preserving private information from two aspects: individual and collaborative activity sensing. For each of the two aspects, the methods are classified into three levels: signal, algorithm, and system. Finally, we discuss the open challenges and provide future directions.

Keywords

Human activity sensing / Privacy-preserving sensing / Activity sensing algorithms / Human sensors / Privacy protection

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (2021YFB3100400), National Natural Science Foundation of China (62302274, 62202276 and 62232010), Shandong Science Fund for Excellent Young Scholars, China (2022HWYQ-038), Natural Science Foundation of Shan-dong, China (ZR2023QF113), and financial support of Lingnan University (LU), China (DB23A4) and Lam Woo Research Fund at LU, China (871236).

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