# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 2 Related Work  
### Crowd-Sensing Data Security  
DAEQ-FL [13] proposed an efficient encryption-based data security protocol that does not require third-party assistance to generate keys. It employs model parameter quantization processing and approximate aggregation combined with threshold-secret-sharing technology to reduce communication costs and computational complexity. EPTD [14] presented an efficient privacy protection method that shields vehicle crowd-sensing systems with double masking and allows for offline uploads while maintaining privacy and accuracy. ESPPTD [15] introduced an improved slice-based privacy protection method that ensures data privacy while simplifying the computation and communication load.  
However, relying solely on encryption methods to protect data security may increase the system burden and pose security risks. A privacy protection scheme for crowd-sensing security that utilizes ciphertext policy attribute-based encryption and access update policies (CP-ABE-UP) was proposed, as well as ID-based signatures for identity authentication and privacy protection [16].  
In response to the impact of malicious users in crowd sensing, some researchers proposed providing services based on the screening of user identity information. A privacy protection scheme utilizing the K-anonymity mechanism was proposed [17]. [PERSON] et al. [18] presented a revocable method for protecting user identity information using group signatures. A non-linkable but accountable identity authentication scheme using pseudonymous signatures was constructed [19]. Reference [20] developed a stronger privacy protection method using efficient protocol signatures. PACE [21] proposed a privacy protection incentive method based on data quality, while reference [22] introduced a personalized privacy protection incentive mechanism based on contracts. Although these studies incorporated mechanisms for screening malicious users, the incurred costs were substantial.  
In the field of Internet of things (IoT) data privacy and security, a number of studies focused on granular disclosure control techniques that provide fine-grained control over data sharing in different contexts. [PERSON] et al. [23] proposed a framework that enables users to specify how and when their data can be shared with different parties based on the specific context or situation, which enhances the privacy and security of IoT devices and applications. The method was shown to be effective in various contexts, and it provides valuable insights into the development of more secure and privacy-preserving IoT systems.  
In existing mobile crowd-sensing applications, data security and user privacy protection methods based on cryptographic techniques generally suffer from high computational complexity and significant system overhead, making them less suitable for resource-constrained mobile terminal devices. Furthermore, the existing methods based on user identity and data filtering also exhibit high computational complexity and do not adequately consider the correlation between users' legitimate identities and data characteristics.