# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 2 Related Work  
In order to provide reliable service, many algorithms have been suggested to protect location privacy in LBS during the past several years. These algorithms are divided into the following categories [22]. One popular strategy is based on spatial and temporal obfuscation such as \(k\)-anonymity, caching schemes and dummy location schemes. In \(k\)-anonymity schemes, the real location is obfuscated and the user is hidden among many users. These algorithms are simple and easy to implement and suitable for most cases. The precision of location, on the other hand, is sacrificed [23]. Caching schemes provide accurate location service, but they cannot meet the demands of real-time services [24]. In dummy location schemes, many false location data is generated and sent to the server as well as the real one, which causes a waste of resources [25]. Another strategy to protect location privacy is the cryptography method, such as homomorphic encryption [26; 27]. These algorithms need high-performance computational capacity to encrypt and decrypt the messages, which takes a lot of time. So the algorithms are not suitable for low-performance equipment and real-time services. In Table 1, we summarise the location privacy preserving schemes.  
Among all the location privacy preserving algorithms, the most widely used algorithm is \(k\)-anonymity. The concept \(k\)-anonymity is first proposed in the field of data publishing for data desensitization [28]. If any arbitrary attribute is contained in at least \(k\) records, the dataset achieves \(k\)-anonymity. This ensures that any individual cannot be easily distinguished by linking attacks. Later, this idea is applied to protect location privacy [29]. In each anonymous set, there are at least \(k\) users. By this means, it protects location privacy from the LBS provider and the probability of tracking a specific user is reduced to \(1/k\).  
Most of the previous studies are based on Euclidean geometric space. However, a user's activity is restricted by road network topology, which brings new challenges in protecting location privacy. A method named \(l\)-diversity is proposed for query privacy preservation based on road network topology [21], where the anonymous set should consist of not only \(k\) users but also \(l\) different road sections. Based on \(l\)-diversity, [PERSON] et al., proposed a new cost function that balanced between the query execution cost and the query quality [30]. An algorithm is proposed to minimize the query execution cost. [PERSON] and [PERSON] introduced an X-Star scheme [31]. It achieves \(l\)-diversity and has good extendibility. To tackle the problem of low anonymous success rate, [PERSON] and [PERSON] proposed a new X-Star scheme based on Hilbert index, named H-Star [32]. In [33], [PERSON] proposed a method that transformed Euclidean geometric space into the road network environment. Considering the road network environment, [PERSON] et al., proposed an anonymous ring scheme to realize location privacy protection [34]. In [35] a voronoi graph based algorithm is proposed. It considers the problem when users are not uniformly distributed and resists the side-weight inference attack.