# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 1 Introduction  
With the development of wireless communication technology and smart mobile devices, many applications and services have emerged and enriched our daily life [1]. Among them, location-based service (LBS) is one kind of the most important and popular services that are widely used [2]. LBS can provide various services such as navigation, query of points of interest (POIs), ride-sharing, location-based games and some derivative applications [3]. Because of these conveniences, people's demand for LBS has greatly increased. LBS brings benefits and convenience to daily life whereas it poses a risk to users' privacy [4]. In order to obtain LBS, mobile users have to send query requests, which are sensitive as they may contain the users' personal information, such as user ID, precise location and query content, to LBS providers [5]. Based on a user's personal information, it is easy to find out private information such as a home address, working place, habits and health status [6, 7]. People are willing to use LBS and provide sensitive information, as they believe that the service providers are trusted. However, the LBS providers are honest but curious about the information. They would like to collect the information uploadedby the users to make optimization of their query algorithm or to sell to other companies. Even if the LBS provider can be fully trusted, there are still some malicious users that may eavesdrop on the channel and get the information of the users. What is worse, once the LBS provider is hacked, the adversary would get the uploaded information of users. The leakage of location privacy has become an issue that cannot be ignored [8]. In 2017, the records of global data leakage and theft reached 1.6 billion, causing huge economic losses and causing consumers' extreme concerns about privacy. In 2019, the overall economic loss of Chinese users was more than 10 billion dollars due to personal information disclosure, fraud and other reasons. In recent years, data abuse events such as Facebook have aroused widespread concern in the society [9]. Due to security system vulnerabilities, up to 87 million Facebook users' private information was leaked, which contains name, contact information, search records, login location, etc. The disclosure of this sensitive information may lead to fraud, theft and other crimes [10]. Therefore, the problem of privacy disclosure needs to be solved urgently as it hinders the development of LBS [11]. In order to solve this problem, many studies focused on addressing how to protect privacy in LBS during the past several years based on location obfuscation and encryption [12], such as \(k\)-anonymity [13], mixed zone [14], caching [15], dummy locations [16], homomorphic encryption [17] and differential privacy [18; 19; 20]. Among them, the most classic location privacy preservation scheme is \(k\)-anonymity. In \(k\)-anonymity scheme, the location of a user sent to the server is a region, namely anonymous set, instead of precise coordinates. There are at least \(k\) users in the anonymous set. The adversary can not distinguish the real location of a specific user from the other \(k-1\) obfuscation locations. The recent researches mostly focus on Euclidean geometric space, where users can be anywhere in the space. However, in real-world situations, a user's activity is restricted by road network topology, especially for vehicle users, which brings new challenges to location privacy protection. As a user is on a specific road, the adversary can guess the road to which the user belongs. Similar to \(k\)-anonymity, [PERSON] introduced the idea of \(l\)-diversity [21]. The anonymous set should consist of not only \(k\) users but also \(l\) different road sections. Although the \(k\)-anonymity and \(l\)-diversity schemes protect location privacy, the uneven distribution of users will lead to the performance degradation of the privacy protection scheme. In the urban scene, there are a large number of vehicles on the main roads, while there are a few vehicles on other roads.