A cloud computing environment provides a cost-effective way for the end user to store and access private data over remote storage using some Internet connection. The user has access to the data anywhere and at any time. However, the data over the cloud do not remain secure all the time. Since the data are accessible to the end user only by using the interference of a third party, it is prone to breach of authentication and integrity of the data. Moreover, cloud computing allows simultaneous users to access and retrieve their data online over different Internet connections, which leads to the exposure, leakage, and loss of a user’s sensitive data in different locations. Many algorithms and protocols have been developed to maintain the security and integrity of the data using cryptographic algorithms such as the Elliptic Curve Cryptography (ECC). This paper proposes a secure and optimized scheme for sharing data while maintaining data security and integrity over the cloud. The proposed system mainly functions by combining the ECC and the Advanced Encryption Standard (AES) method to ensure authentication and data integrity. The experimental results show that the proposed approach is efficient and yields better results when compared with existing approaches.

In data publishing, privacy and utility are essential for data owners and users respectively, which cannot coexist well. This incompatibility puts the data privacy researchers under an obligation to find newer and reliable privacy preserving tradeofftechniques. Data providers like many public and private organizations (e.g. hospitals and banks) publish microdata of individuals for various research purposes. Publishing microdata may compromise the privacy of individuals. To prevent the privacy of individuals, data must be published after removing personal identifiers like name and social security numbers. Removal of the personal identifiers appears as not enough to protect the privacy of individuals. Κ-anonymity model is used to publish microdata by preserving the individual’s privacy through generalization. There exist many state-of-the-arts generalizationbased techniques, which deal with pre-defined attacks like background knowledge attack, similarity attack, probability attack and so on. However, existing generalization-based techniques compromise the data utility while ensuring privacy. It is an open question to find an efficient technique that is able to set a trade-off between privacy and utility. In this paper, we discussed existing generalization hierarchies and their limitations in detail. We have also proposed three new generalization techniques including Conventional Generalization Hierarchies (CGH), Divisors Based Generalization Hierarchies (DBGH) and Cardinality-Based Generalization Hierarchies (CBGH). Extensive experiments on the real-world dataset acknowledges that our technique outperforms among the existing techniques in terms of better utility.

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.

Vehicles become more advanced and smarter due to advancements in technology in the modern world. Every person now a days, demand a smart vehicle due to their automobility and smart controls. This is all possible through advancements in VANET (Vehicular Adhoc Network) and the Internet of Vehicles (IoV). Vehicles in the VANET are highly connected to each other and this thing can cause security, safety, and privacy risks for the asset itself and driver also. It can become a reason of major threat. And these threats can occur due to tracing the location of the vehicle. Existing techniques like group-based shadowing schemes, obfuscation, silent periods, and mix-zone have preserved privacy of location somehow, but don’t have a good QoS and optimized efficient security. To overcome these issues, we introduced a new privacy framework, which is an improvement of the existing shadowing scheme. We proposed a computationally efficient group leader selection process based on centeredness, rule obeyed, and OBU resources, reducing overhead by 20%, integrating FL with DP to preserve data privacy without sacrificing utility, and achieving a 15% improvement in location accuracy under privacy constraints, validating the scalability and robustness of the framework through extensive simulations involving up to 300 vehicles. Group Leader is used as an optimization of the overall framework including efficiency and implementation of the scheme. This scheme increases privacy if the number of vehicles also increases, and this thing makes our scheme more scalable. This scheme overcomes the many drawbacks of existing techniques like a higher tracing ratio in shadowing schemes, totally depending on the group leader, and reduced utility of all schemes based on distances. The most important thing, the single point of failure in the group leader base shadowing scheme is overcome by using local federated learning with differential privacy. Validation results of our proposed scheme showed that it outperformed the current schemes mainly based on group leader.