# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 4 Application of Smart Mobility with Big Data  
### Security  
Data generated by smart mobility sometimes contain personal or sensitive information, so security is emphasized. There is also a risk of exposure while storing the generated data on a data server in real time. In this section, we review possible security issues that may arise between data transmission, referencing papers on the security of big data systems. For example, the authors of [59] suggested a secure data collection method when generating data using the Internet of Vehicles (IoV). Collected data are stored in HDFS with three components: the vehicle node, sink node, and big data center. The node sends the data to the sink node to reduce storage. Encryption and decryption are performed using the symbol _key_vc_, which is generated by a single sign-in algorithm. Next, the sink node checks the _key_vc_ and allows access to the big data center if appropriate. The authors of another study [60] proposed an automated, secure continuous cloud service availability framework for smart connected vehicles, enabling intrusion detection mechanisms against security attacks and providing services that meet users' quality-of-service (QoS) and quality-of-experience (QoE) requirements. The proposed framework detects attacks through data traffic analysis, reduction, and classification techniques for requests that may occur during intrusions. The authors of [61] focused on the privacy of ride sharing. Ride-sharing companies represent a risk of personal information infringement because they require users to disclose sensitive details about travel time and routes, as well as their pickup and dropoff locations. The authors proposed an encryption method using binary vectors. The authors of another study [62] found that collecting real-time traffic situation data using a connected vehicle and offloading them wirelessly to an edge computing device (ECD) increases the risk of personal information leakage, resulting in tracking, identity modulation, and virtual vehicle hijacking. The authors applied the non-dominated sorting algorithm-2 (NSGA-2) to realize multipurpose optimization to reduce execution time and energy consumption and prevent privacy conflicts. The authors of [63] proposed security- and privacy-based access control (SPBAC) for connected vehicles. The proposed model allows security officials to access information, privileges, and roles rather than only roles for officials traveling in vehicles with the same fleet. Furthermore, the authors of [64] proposed a mechanism for randomization of data streams to ensure personal privacy. The proposed mechanism is data-adaptive and adjusts the noise for data correlations. The authors of another study [65] suggested a threshold-based image extraction solution for ITS and proposed an algorithm that uses Fast R-CNN to divide images according to importance levels and protect personal information using high thresholds for sensitive parts. The authors of [66] suggested a blockchain framework to address privacy and security issues associated with big data and smart mobility. The proposed plan is called blockchain smart mobility data (BSMD) and is built according to the following principles: (a) Regarding user privacy, multiple DIDs are assigned to a single user, making it difficult to anonymize information and associate data. (b) Data ownership allows users to own a digital identifier and cancel the connection. (c) Concerning data transparency and audibility, anyone can access the ledger, and nodes can track all transactions related to the information. (d) For granular access control, smart contracts define what information nodes share. The authors of another study [67] proposed a multilayer blockchain framework for BSMD to address privacy, security, management, and scalability issues. It was developed as a six-layer model that defines the flow of mobility information, and open-source code is available in the transportation community.