The Internet of Things (IoT) distributes private data from remote sensors. IoT block chain has been used for decentralized cloud health record security. In health care environment, the Personal Healthcare reports include sensitive information that must be highly protected. The Big Data environment in healthcare data analysis poses challenges for collecting sensitive information from Electronic Personal Healthcare Records (EPHR) to maintain privacy. The unauthorized policy allows hostile persons to steal healthcare record-sharing keys and data. Personal data management requires privacy and security. Current privacy rules are overly insensitive, resulting in poor performance and lead time complexity. Calculating environmental illness burden requires exposure-risk response interactions. We propose an EPHR to address these difficulties. In big data healthcare situations, the EPHR-sensitive data prediction system secures sensitive data using a Quasi-Based Sensitive Attribute Identifier and sophisticated block chain security. The sensitive Scaling Impact Rate is used to identify sensitive and non-sensitive marginal values. Marginal value-based Quasi-based sensitive attribute identification Algorithm (QBSAI) evaluates sensitive feature relations and classifies sensitive and insensitive traits. The Key Aggregation policy is used for Blockchain security in the healthcare sector. The suggested approach outperforms current privacy preservation techniques in sensitive data prediction systems for block chain privacy and security. Thus, the proposed algorithm produces a higher security performance than previous methods.