In the era of big data, clustering and data mining have become essential tools for uncovering patterns and insights from vast datasets. However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. This paper proposes N2P-CM, a novel privacy-preserving framework designed to protect sensitive information during the entire clustering and mining lifecycle. Unlike existing methods that focus on partial aspects of security or apply generic encryption techniques, N2P-CM integrates five innovative and synergistic modules: Sensitive Feature Obfuscation, Adaptive Trust Weight Aggregation, Compressed Secure Semantic Embedding, Differential Traceable Execution Engine, and Blockchain Auditable Ledger. Each module contributes a distinct layer of privacy and accountability, ranging from feature-level data transformation and federated trust scoring to secure semantic encoding and traceable execution logging with blockchain support. We provide formal definitions and algorithms for each module and demonstrate their integration in a unified architecture. Extensive simulations using real-world datasets validate the efficacy of N2P-CM, showing that it achieves strong privacy guarantees with minimal degradation in clustering accuracy. This research contributes a comprehensive and modular solution to the growing need for privacy-preserving analytics in sensitive domains such as healthcare, finance, and smart cities.

At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.