Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection
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 propose...
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| Format: | Article |
| Language: | English |
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Growing Science
2025-01-01
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| Series: | International Journal of Data and Network Science |
| Online Access: | https://www.growingscience.com/ijds/Vol9/ijdns_2025_11.pdf |
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| author | Haythem Hayouni |
| author_facet | Haythem Hayouni |
| author_sort | Haythem Hayouni |
| collection | DOAJ |
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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. |
| format | Article |
| id | doaj-art-96f46e8db79d4cdabfac68a19703f5ad |
| institution | Kabale University |
| issn | 2561-8148 2561-8156 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj-art-96f46e8db79d4cdabfac68a19703f5ad2025-08-20T03:24:43ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562025-01-019334535610.5267/j.ijdns.2025.6.002Enhancing privacy in clustering and data mining: A novel approach for sensitive data protectionHaythem Hayouni 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.https://www.growingscience.com/ijds/Vol9/ijdns_2025_11.pdf |
| spellingShingle | Haythem Hayouni Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection International Journal of Data and Network Science |
| title | Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection |
| title_full | Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection |
| title_fullStr | Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection |
| title_full_unstemmed | Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection |
| title_short | Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection |
| title_sort | enhancing privacy in clustering and data mining a novel approach for sensitive data protection |
| url | https://www.growingscience.com/ijds/Vol9/ijdns_2025_11.pdf |
| work_keys_str_mv | AT haythemhayouni enhancingprivacyinclusteringanddatamininganovelapproachforsensitivedataprotection |