An Efficient Activity Recognition Framework: Toward Privacy-Sensitive Health Data Sensing

IEEE Access

Mohammed Gh. Al Zamil¹  Ahmed F. Aleroud¹  Samer Samarah¹  Majdi Rawashdeh²  Atif Alamri³  Mohammed F. Alhamid³ 

[1] Department of Computer Information Systems, Yarmouk University, Irbid, Jordan;Department of Management Information System, Princess Sumaya University for Technology, Amman, Jordan;Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;

关键词: Internet of Things;    data mining;    data privacy;    healthcare;    smart home;   

DOI  :  10.1109/ACCESS.2017.2685531

来源: DOAJ

【 摘 要 】

Recent advances in wireless sensor networks for ubiquitous health and activity monitoring systems have triggered the possibility of addressing human needs in smart environments through recognizing human real-time activities. While the nature of streams in such networks requires efficient recognition techniques, it is also subject to suspicious inference-based privacy attacks. In this paper, we propose a framework that efficiently recognizes human activities in smart homes based on spatiotemporal mining technique. In addition, we propose a technique to enhance the privacy of the collected human sensed activities using a modified version of micro-aggregation approach. An extensive validation of our framework has been performed on benchmark data sets yielding quite promising results in terms of accuracy and privacy-utility tradeoff.

【 授权许可】

Unknown