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.There are several privacy and security issues in existing systems, such as identity and location disclosure, availability, and authenticity. Smart parking systems utilize third-party parking recommender systems to offer customized parking space recommendations to its users based on user’s past parking experience. However, indiscriminately sharing a user’s data with a third party recommendation system may violate their personal information because their activity and node mobility can be deduced from their previous paring experience. Another problem with existing solutions is that most distributed systems need a third party to anonymize user data to preserve the user’s privacy. Therefore, this article provides three solutions that address the problems mentioned above; at first, based on elliptic curve cryptography (ECC), we proposed the mutual authentication mechanism using HMAC to provide anonymity and integrity during communication; secondly, given the risks to security and privacy posed by untrustworthy third parties, we used local differential privacy, which uses the Laplace distribution technique to add noise randomly and eliminates any necessity for a third party for data perturbation. And thirdly, in addition to LDP , we utilize the IOTA distributed ledger technology 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.