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@article{207680,
        author = {Adeeba Imtiyaz and Dr Pertik Garg},
        title = {Enhancing Healthcare Data Privacy with Client-Side Anonymization: A Lightweight, Rule-Based Architecture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {2115-2129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207680},
        abstract = {As more health-related information is moved to cloud, so are concerns about patient-privacy, even with traditional encryption and access control methods, as sensitive patient details are still at risk of being leaked. Current anonymization methods often involve complicated infrastructure or a lack of visibility into whether privacy is being ensured. This paper proposes a lightweight, rule-based, client-side anonymization system for structured healthcare datasets that anonymizes data locally before it is stored or shared in cloud environments. The system uses keyword-based attribute classification to identify sensitive fields and applies masking, generalization, temporal reduction, and suppression accordingly, leaving non-sensitive attributes unchanged. Anonymization effectiveness is validated using k-anonymity, l-diversity, a custom information-loss metric, and analytically modeled record-linkage attacks under varying attacker knowledge levels. Experiments on synthetic healthcare datasets of varying sizes (2000, 5000, and 10000 records) assessed privacy protection, utility preservation, and scalability. Results showed minimum equivalence class size of k=9-45 and Identity Disclosure Risk of 0.64%-1.60%respectively, implying an effective privacy-utility balance with robust resistance to record-linkage attacks even as attacker capability increases, demonstrating that measurable privacy preservation is achievable without computationally intensive infrastructure.},
        keywords = {Data anonymization, healthcare data privacy, k-anonymity, l-diversity, Information Loss, re-identification risk, client-side privacy, cloud data sharing.},
        month = {August},
        }