Name
Title | ||
|---|---|---|
A Data Privacy-preserving Method for Students’ Physical Health Monitoring by Using Smart Wearable Devices |
Abstract | ||
|---|---|---|
Nowadays, physical fitness of young people has been widely concerned by all fields of research. Using smart wearable devices to monitor students' physical fitness level is a good choice. However, due to its limited computing performance, the use of smart wearable devices faces the risk of privacy disclosure. We propose a privacy-preserving framework for smart wearable devices, and an improved algorithm based on differential privacy according to the characteristics of smart wearable devices. Through a shielding condition, we choose the appropriate data to publish, which reduces the possibility of attackers to obtain users' privacy. In the following tests, we take approximate entropy as the indicator to reflect the security of results, and it finally conclude that compared with the traditional method, the algorithm proposed in this paper has a better ability of privacy preserving. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00021 | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Keywords | DocType | ISBN |
differential privacy,data publishing,smart wearable devices,privacy-preserving | Conference | 978-1-7281-6610-0 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghui Yang | 1 | 0 | 0.34 |
| Junqi Guo | 2 | 61 | 15.07 |
| Ludi Bai | 3 | 0 | 0.34 |