# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 3 Related Work
### Trust Management Models in VANETs  
The Methods for managing confidence depending on a trusted Third-Party Partnership (TTP) are proposed. Reference [13] are developing a chief evaluation scheme for carriage and RSUs to define and remove hackers from the network. It provides the trust and credibility details of unethical records to ensure the prolonged reliability of VANETs [52]. introduced a scheme to test the reliability of messages. The main purpose of this scheme is to focus on robustness and error tolerance of the fleeting unavailability of the main server. In [15] the scheme proposes a Reputation-based Global Trust Establishment (RGTE) scheme for exchanging trust information in VANETs based on law. Above all, confidence management approaches based on TTP need a reputation server to create a framework for managing world reputations. Nonetheless, these infrastructures can be a goal for hackers, the total cost of maintaining regular TTP functionality is high and may be higher or recovery from accessing. The centralized networks are also not good for VANETs. Onemore approach to developing secure protection in VANETs is the use of hubs. Reference [16] develop a trust-conscious research learning automated intrusion detection system (T-CLAIDS) for secure management in VANETs. It confides on the high size of vehicles in a given area to build a classifier that can be shared to the so-called Collaborative Trust Index (CTI) to detect any illegal behavior. Table 2 shows VANETs location privacy schemes used in different papers. Table 3 shows comparison between VANETs location privacy schemes. Reference [58] use the credibility of network services and develop a two-phase model to enable and to cooperate during combine formation and to identify illegal behavior after combine formation. Reference [59] is developing an effective and insignificant intrusion finding system named AECFV to secure VANETs from the most dangerous attacks. AECFV uses a stable clustering algorithm to pick the Cluster Head (CH) based on automobile and belief level. Reference [60] use Petri Networks to model dynamic automobiles capable of making decisions preceding the reliability of warning mails based on a successful collabora \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref** & **Category** & **Salient Feature** & **Goals** & **Limitations** & **Parameter** \\ \hline
[53] & Silent period & Enhances anonymity, transmission control capability & Address location privacy, tracking attacks & No comparison & Anonymity set size \\ \hline
[54] & Mix-group & system initialization, key generation, pseudonym grouping leaving revocation protocol & exploit the meeting opportunity for pseudonym changing, improve the location privacy & Many assumption for implementation & Location privacy \\ \hline
[55] & & OBU safety message generation, verification OBU fast tracing & support a multi-level confidentiality, privacy & Limited analysis with threat model & Multi-level authority \\ \hline
[56] & Silent period & Pseudonym changing at small social spot, Pseudonym changing at large social spot & Achieving the location privacy based on pseudonyms changing technique & No comparison with other methods & Location privacy, Anonymity \\ \hline
[46] & Mix-group & System initialization and key generation, Group join, Pseudonyms exchanging, Group leaving, Protocol, Protocol, Unfolding tracking & Exploit the meeting opportunities for pseudonym changing, Improve the location privacy preservation & Many assumptions needed to understand implementation & Location privacy \\ \hline
[57] & Obfuscation Scheme & The true path of the vehicles is hidden over long trajectories & Location tracking & Location entropy & tracking success \\ \hline \hline \end{tabular}
\end{table} 
Table 2: VANETs location privacy schemes used in different papers.