In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.

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The term blockchain pasteurizes the chain of blocks like linked lists which will be linked to the previous block. A blockchain is a distribted ledger of digital transactions which is decentralized and no individual, team or company has its controllability over it. However, with time, the use of blockchain has come into existence. It has wide range of applications including risk management, credit money, Internet of Things ( IoT), social networks, and financial resources. Blockchain has many benefits like decentralization, persistency, anonymity, audit ability. Blockchain is trustable one and it is very efficient. In particular, this paper gives us the detailed information about the blockchain and its applications.

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