# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 1 Introduction  
The emergence of mobile crowdsensing technology [1; 2; 3] comes from the integration of sensors and embedded devices. Handheld devices can acquire useful information in the real world by collecting, uploading and sharing data [4; 5]. In various scenarios like social recommendations (e.g., food or hotel recommendations) and real-time monitoring of the surrounding environment (e.g., noise level monitoring) [6; 7], an application uses a mobile device's perception capabilities to enhance data credibility, thereby minimizing the expenses of installing sensor equipment. However, its implementation may inadvertently expose sensitive information about mobile users, including social connections and location data [8; 9]. The platform's task allocation relies on the user's location, causing the data collected by the user's device to potentially reveal their trajectory or other details. For instance, Google Maps utilizes \"anonymous\" location data from drivers to create real-time maps, yet it inadvertently discloses the driver's route and location. An attacker with prior knowledge will use the known information to mine the user's privacy to threaten the user's personal safety. Therefore, when designing perceptual service applications, the first consideration is to protect the privacy and security of collectors. Only by designing a reasonable and effective data protection mechanism can users accept more tasks. Users may disclose their own sensitive information when participating in crowdsensing: a malicious cloud platform may use user information to make profits, which will cause immeasurable losses to users and thus dampen the enthusiasm of mobile users for participating in crowdsensing. Therefore, how to solve the privacy protection problem in crowdsensing is crucial; privacy protection has gradually been paid great attention by scholars, and a variety of privacy protection methods have been proposed [10; 11; 12; 13; 14]. In 2003, [PERSON] first proposed the concept of location privacy protection [15]. Privacyprotection is primarily focused on concealing users' identity and location information for safeguarding their confidentiality [16; 17]. Currently, common privacy protection methods include anonymity-based methods, reputation-based methods and differential privacy-based methods. There are several mobile crowd awareness schemes using anonymity technology to protect privacy data at home and abroad [18; 19; 20; 21], such as k-anonymity technology [22]. The main idea of the k-anonymity technique proposed by [PERSON] and [PERSON] is as follows: Assuming that the attacker is faced with massive data and there are a certain number of quasi-identifiers (such as age, gender, salary, etc.) in the massive data of which records cannot be identified, the attacker can use prior knowledge to narrow the data to a certain range of equivalence classes that satisfy their prior knowledge but cannot lock the attack target from the equivalence class [22; 23]. This technology uses equivalence classes to protect personal privacy, and parameter k can measure the maximum information disclosure risk that users can bear [24]. Nevertheless, anonymization is insufficient for privacy preservation, since mobile users may be traced via travel routes and social relations. Meanwhile, despite the efficacy of k-anonymity technology in data protection, its reliance on the attacker's background knowledge poses a significant vulnerability. If faced with a new attacker possessing unknown prior knowledge, the attacker can potentially differentiate various records within the published equivalent dataset beyond the predicted scope, leading to deanonymization [25]. So, the security of the k-anonymous model depends on the knowledge possessed by the attacker. And since it is impossible to prove that the algorithm can evaluate the mathematical process of privacy levels, it is not a very perfect privacy protection strategy.