# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 1 Introduction
Reputation-based approaches are popular in mobile crowdsensing for allocating tasks to mobile users, but they have their inherent weaknesses. Reputation-based mechanisms [19; 26; 27; 28] can assign tasks to mobile users. However, the mechanism reliant on reputation is vulnerable due to its dependency on a trusted third party (TTP) for managing reputation, which makes it vulnerable to reputation-linking attacks, in which anonymous mobile users can be reidentified based on their reputations. The SPOON framework, proposed by [PERSON] et al., addresses this weakness by utilizing proxy re-encryption and BBS signature technology to safeguard sensitive information of mobile users and customers. Named SPOON [6], this framework enables registered customers and mobile users to anonymously demonstrate their ability and trustworthiness for participating in service perception tasks, ensuring the security of the tasks and reports. However, the effectiveness of this technology still relies on the guarantee provided by a trusted third party. If this third party breaches trust, the data remain vulnerable and unprotected. The emergence of differential privacy technology effectively addresses the aforementioned issues. Differential privacy was originally used in the field of statistical databases (database). [PERSON] [24; 25; 29] first applied differential privacy technology to the statistical database field with the aim of safeguarding individuals' privacy information when publishing statistical data. Differential privacy is advantageous as it does not rely on an attacker's prior knowledge and can be mathematically proven using a quantitative evaluation method. This technology has gained significant attention from privacy protection researchers and has been implemented in various areas, including privacy-protected data publishing and mining. While designing privacy protection strategies to ensure the security of data, we should complete the task perception related to data collection as much as possible. To address this problem, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the lowest paid task allocation strategy. Considering the privacy protection requirements of users at different levels, we have designed various levels of differentiated privacy protection mechanisms by incorporating the privacy budget allocation strategy. In addition, we consider a reward mechanism in task assignment to balance the effectiveness and security of user-uploaded location data. Experimentsdemonstrate that the strategy proposed in this paper not only safeguards the data but also empowers users to freely select the level of privacy protection. In summary, this paper makes the following contributions:  
* Privacy protection in perception: We investigate the privacy protection problem in perception-oriented application services. Our aim is to design privacy protection strategies ensuring data security, with the capability to comprehensively fulfill the task of data collection perception. * Personalized data privacy protection: We propose a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the lowest paid task allocation strategy. Considering the varying levels of user privacy protection needs, we design different-level privacy protection mechanisms by incorporating the privacy budget allocation strategy. We also consider a reward mechanism in task allocation to balance the effectiveness and security of user-uploaded location data. * Extensive evaluation: We conduct thorough evaluations on various real-world datasets. The results demonstrate that our proposed strategy not only safeguards the data but also allows users to freely choose their preferred level of privacy protection. The remainder of this paper is organized as follows. After reviewing the related works in Section 2, we introduce the model framework in Section 3.