Smart City Gnosys
Smart city article details
| Title | Privacy-Aware Data Fusion And Prediction For Smart City Services In Edge Computing Environment |
|---|---|
| ID_Doc | 43140 |
| Authors | Qi L.; Chi X.; Zhou X.; Liu Q.; Dai F.; Xu X.; Zhang X. |
| Year | 2022 |
| Published | Proceedings - IEEE Congress on Cybermatics: 2022 IEEE International Conferences on Internet of Things, iThings 2022, IEEE Green Computing and Communications, GreenCom 2022, IEEE Cyber, Physical and Social Computing, CPSCom 2022 and IEEE Smart Data, SmartData 2022 |
| DOI | http://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00043 |
| Abstract | With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE. |
| Author Keywords | Data fusion and prediction; Edge computing; Locality-Sensitive Hashing; Privacy; Smart city |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
43141  | 0.891 | Qi, LY; Hu, CH; Zhang, XY; Khosravi, MR; Sharma, S; Pang, SN; Wang, T | Privacy-Aware Data Fusion And Prediction With Spatial-Temporal Context For Smart City Industrial Environment | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 17, 6 (2021) |
| 29766 | 0.86 | Yadav P.K.; Pandey S.; Singh P.; Pandey P. | Hybrid Laplace-Gaussian Differential Privacy To Secure Data Aggregation In Edge-Iot Systems | 2024 IEEE 1st International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024 (2024) |
| 43116 | 0.857 | Bhadauria S.; Kumar M.; Singh N.P. | Privacy Preserving And Performance Improvement In Edgecomputing Using Machine Learning | 2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022 (2022) |
| 3809 | 0.853 | Yao A.; Pal S.; Li X.; Zhang Z.; Dong C.; Jiang F.; Liu X. | A Privacy-Preserving Location Data Collection Framework For Intelligent Systems In Edge Computing | Ad Hoc Networks, 161 (2024) |