At present, real estate markets hold and contribute a fair share of the Gross Domestic Product (GDP) of any country. The real estate transactions generate revenue in stamp duties, which goes directly into the government kitty. However, this suffers from land document forgery and fraudulent activities due to a lack of an efficient, distributed, and completely digital system. This paper proposes a blockchain-based framework for digitizing property transactions that mitigate the risk of document forgery and other fraudulent activities. Over time, the amount of these transactions can become overwhelming, thus increasing the number of blocks. The proposed framework is based on blockchain technology to decentralize the complete ecosystem. The framework encompasses all major activities for property transactions. The framework uses the InterPlanetary File System (IPFS) that is a Peer-to-Peer (P2P) swarm network, to integrate different region registry offices in the state/country seamlessly. In order to make it secure, a consensus algorithm is proposed that reduces overhead transmissions for multicasting nodes by about 50%. The message exchange communication overhead also reduces by 54.86%. The time taken for the consensus algorithm is around 53.7% lesser than the Proof-of-Work mechanism in which all nodes participate in consensus and 10% lesser than the existing load-based approach.

The healthcare sector has experienced significant transformations due to the expansion of industrial edge computing and the Internet of Things (IoT), resulting in an increased volume of distributed healthcare data. Nonetheless, preserving the privacy and security of healthcare data remains a paramount concern. This paper proposes a blockchain-based approach BlockDP-FL that integrates deep learning (DL) with federated learning (FL) enhanced by differential privacy (DP) to protect electronic health records (EHRs). This study leverages a Bi-directional Long Short-Term Memory (BiLSTM) network to analyze data and categorize users as normal or abnormal. By integrating DP within the FL framework, it ensures secure data management while accurately identifying and eliminating abnormal users, safeguarding critical healthcare information. The proposed framework, developed in Python, delivers improved classification accuracy and performance over existing methods while strengthening data privacy.