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Acute subdural empyema is a surgical emergency. It is life threatening for the patient. It has to be evacuated as soon it is diagnosed. But subdural empyema in a COVID 19 patient is uncommon. Its management put the surgical team to a new dilemma. On one side the patient’s life was at risk and on the other side the whole surgical team might get infected.In this case report, we describe such a case which saved the patient’s life at same time many doctors, nurses and OT attendants became infected. Abbrevaitions: OPD- Out patient department, COVID 19-Corona Virus Disease 2019, NINS- National Institute of Neurosciences, RT-PCR- Reverse transcription polymerase chain reaction. SDE- Subdural empyema, PNS- para nasal sinuses Bang. J Neurosurgery 2021; 10(2): 206-209

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.

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