Dynamic privacy measurement model and evaluation system for mobile edge crowdsensing
To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage, a dynamic privacy measurement (DPM) model was proposed.A...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2021-02-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021016 |
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| Summary: | To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage, a dynamic privacy measurement (DPM) model was proposed.A structured representation of data obtained by a user participating in a sensing task was introduced and was transformed it into a numerical matrix.Then privacy attribute preference and timeliness were presented to quantify the dynamic privacy changes of data.With this, personalized privacy thresholds of users based on the numerical matrix were reasonably calculated.Finally, differential privacy processing was performed on the numerical matrix, and a model evaluation system was designed for the proposed model.The simulation results show that the DPM model was effective and practical.According to the given example, a data utility of approximately 0.7 can be achieved, and the degree of privacy protection can be significantly improved as the noise level increases, adapting to the MECS of IoT. |
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| ISSN: | 2096-109X |