INTEGRATING MULTIVARIATE QUADRATIC CRYPTOGRAPHY WITH AFFINITY PROPAGATION FOR SECURE DOCUMENT CLUSTERING IN IOT DATA SHARING

Abstract

Background information: Secure document clustering is now more important than ever because to the exponential explosion of data brought forth by IoT systems in business, smart cities, and healthcare. In order to improve security and efficiency, this study suggests a system that combines Multivariate Quadratic Cryptography (MQC) with Secure Document Clustering (SDC) and Affinity Propagation (AP). Techniques include clustering with AP and encryption with MQC. The technology seeks to guarantee effective clustering while protecting data. The findings demonstrate that the suggested approach greatly increases accuracy, scalability, and security.
Methods: This study ensures security and efficiency in IoT data clustering by combining AP for adaptive clustering with MQC for robust encryption. To ensure that critical papers are safely arranged, AP is used to cluster the encrypted data. Scalability, security, clustering efficiency, and computing overhead were among the performance indicators that were assessed in order to validate the system.
Objectives: By combining Multivariate Quadratic Cryptography (MQC) with Affinity Propagation (AP), this study aims to create a secure document clustering framework that will improve data confidentiality and clustering effectiveness in Internet of Things scenarios. The framework is appropriate for managing delicate IoT data-sharing problems since it attempts to enhance security, scalability, and clustering accuracy while resolving performance concerns like computational overhead.

Results: All metrics show that the suggested system (MQC + AP + SDC) performs better than the others: 95% overall accuracy, 94% scalability, 95% clustering efficiency, and 95% security. This combination is the best at managing safe and effective document clustering, outperforming more conventional methods such as standalone MQC, AP, and their mixtures.
Conclusion: MQC's combination with AP and SDC results in a high-performance, well-balanced system for safe IoT document clustering. It outperforms earlier methods by increasing security by 95%, accuracy by 95%, and scalability by 94%. The technique works well to improve clustering efficiency while preserving the secrecy and integrity of the data.