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Privacy-Preserving QoS Forecasting in Mobile Edge Environments
- Source :
- IEEE Transactions on Services Computing. 15:1103-1117
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- We propose a novel privacy-preserving QoS forecasting approach - Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments). Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users' accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.
- Subjects :
- Information privacy
Information Systems and Management
Computer Networks and Communications
Computer science
business.industry
Quality of service
computer.software_genre
Computer Science Applications
Computer Science::Performance
Hardware and Architecture
Server
Computer Science::Multimedia
Computer Science::Networking and Internet Architecture
Collaborative filtering
Differential privacy
Noise (video)
Enhanced Data Rates for GSM Evolution
Web service
business
computer
Computer Science::Cryptography and Security
Computer network
Subjects
Details
- ISSN :
- 23720204
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Services Computing
- Accession number :
- edsair.doi...........cab860d8b490c89d471f84007b13cd41