Supply chain management (SCM) is essential for a company’s faster, efficient, and effective product life cycle. However, the current SCM systems are insufficient to provide product legitimacy, transaction privacy, and security. Therefore, this research proposes a secure SCM system for the authenticity of the products based on the Internet of Things (IoT) and blockchain technology. The IoT-enabled Quick Response (QR) scanner and the blockchain-integrated distributed system will allow all the SCM stakeholders to begin secure and private transactions for their products or services. Resulting, the consumer will receive an authentic and genuine product from the original producer. A lightweight asymmetric key encryption technique, i.e., elliptic curve cryptography (ECC) and Hyperledger Fabric-based blockchain technology with on-chain smart contracts are applied for distributed IoT devices to make the authentication process faster and lighter. Each SCM stakeholder is registered by the service provider and receives corresponding public and private keys, which will be used for the authentication process of the participants and IoT devices. The authenticated QR scanner records all transactions on the blockchain. Consequently, there will be no human intervention for the SCM transactions. The security and scalability analysis demonstrates that the proposed system is more secure and robust than other state-of-the-art techniques.

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a $K$-Nearest Neighbour ($K$-NN) classifier with IoT data from various entities. This paper proposes secure $K$-NN, which provides a privacy-preserving $K$-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure $K$-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure $K$-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure $K$-NN protects sensitive data privacy for each P and C. The secure $K$-NN achieved $97.84\%$, $82.33\%$, and $76.33\%$ precisions on BCWD, HDD, and DD datasets. The performance of secure $K$-NN is precisely similar to the general $K$-NN and outperforms all the previous state of art methods.

The voting system is the process to take the opinion of people to run the constitution properly. Fairness, independence, and unbiasedness should be present in the voting system. Hence, it must be a transparent and secured process so that everybody can express their own opinion freely. Worldwide vote manipulation is an intriguing problem in existing voting systems. Since people in different countries are using digital technology in the voting process (e.g., Optical Scan Voting system, Internet Voting system, Electronic Voting system) instead of traditional way (e.g., Ballot Box). Only digitization could not solve the issues completely. Because still there are numerous ways to manipulate or tamper digital technology and hamper the voting process. To build a secure electronic voting environment, we introduce an application of blockchain technology as a service for the distributed electronic voting system. With the use of blockchain, we achieve data integrity which is a necessary attribute of a voting environment. The anonymity of the voters, privacy, and security of the voting environment is the main goal of this work. Through the design of our system and with the help of blockchain we have solved all the security issues in the voting environment.

We study the problem of privacy preservation in multiple independent data publishing. An attack on personal privacy which uses independent datasets is called a composition attack. For example, a patient might have visited two hospitals for the same disease, and his information is independently anonymized and distributed by the two hospitals. Much of the published work makes use of techniques that reduce data utility as the price of preventing composition attacks on published datasets. In this paper, we propose an innovative approach to protecting published datasets from composition attack. Our cell generalization approach increases both protection of individual privacy from composition attack and data utility. Experimental results show that our approach can preserve more data utility than the existing methods.