A Quality-Aware Data Collection Scheme with Privacy for Cyber-Physical Immersive Networking Systems

In pursuit of an immersive virtual experience within the Cyber-Physical Immersive Networking Systems (CPINS), the construction of scenarios often requires a considerable amount of real-world data. Mobile Crowd Sensing (MCS) represents one of the effective methods during data collection of Metaverse. However, privacy-preserving and quality-aware data collection are two critical and contradictory issues in MCS, because privacy-preserving requires hiding as much personal information about users as possible, while quality-aware data collection requires learning as much information about users as possible to recruit high-quality users for data collection. To this end, we propose a Privacy-Preserving and Reputation Calibration based Quality-aware Data Collection (PPRC-QDC) scheme. In the PPRC-QDC scheme, a two-tier truth discovery is proposed to acquire high-quality data. More importantly, a privacy-preserving method is proposed to recognize the users’ reputations by comparing the weights with the trusted users rather than the data to effectively identify the honest users. Finally, theoretical analysis confirms our PPRC-QDC has stronger privacy preservation and robustness capability. Extensive experiments conducted on real-world datasets demonstrate that under privacy preservation, our PPRC-QDC scheme recognizes users’ reputations with an accuracy of 91.5%, and improves the quality of data collection by 11.0%-12.1%.