Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.