In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.

The Energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile Crowd Sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multi-source data and complex workers. To address this, we propose a Completion Rate Learning based Multi-Armed Bandit reverse Auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a Spatial-Temporal Upper Confidence Bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the Dual-Stage Data Estimation Mechanism (DSDEM) and Long-Term and Short-Term Memory Learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.