A Secure and Privacy Preserved Parking Recommender System Using Elliptic Curve Cryptography and Local Differential Privacy

The privacy preservation has received considerable attention from organizations as the growing population is apprehensive regarding personal data being preserved. Smart Parking is a parking strategy that combines technology and human innovation in an effort to use as few resources as possible (such as time and space) to achieve faster and easier parking spots of vehicles. Smart parking systems utilize third-party parking recommender systems to offer customized parking space recommendations to its users based on their past parking experience. However, indiscriminately sharing a user’s data with a third party recommendation system may expose their personal information. As their activity and node mobility can be deduced from previous paring experience. There are several privacy and security issues in existing systems, such as identity and location disclosure, availability and authenticity issues. Another problem with existing solutions is that most distributed systems need a third party to anonymize user data for privacy preservation. Therefore, this article fills the described research gaps by introducing parking recomender systems using Local Differential Privacy (LDP) and Elliptic Curve Cryptography (ECC). Based on ECC we proposed the mutual authentication mechanism using Hash-based message authentication code (HMAC) to provide anonymity and integrity during communication. Moreover, given the risks to security and privacy posed by untrustworthy third parties. We used LDP which uses the Laplace distribution technique to add noise randomly and eliminates any necessity for a third party for data perturbation. In addition to LDP, we utilized the IOTA distributed ledger technology (DLT) to provide a new level of security that ensures immutability, scalability, and quantum secrecy and decentralized the system. Our experiments demonstrate that, in addition to preserving the driver’s privacy and security, our proposed model has low storage overheads, computation, and communication costs.