Publication: Privacy-Preserving Breast Density Classification in Mammograms Using Fuzzy C-Means and Homomorphic Encryption
3
0
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105007524547
Journal Title
2025 17th International Conference on Knowledge and Smart Technology Kst 2025
Start Page
376
End Page
381
Rights Holder(s)
SCOPUS
Bibliographic Citation
2025 17th International Conference on Knowledge and Smart Technology Kst 2025 (2025) , 376-381
Suggested Citation
Mongkolluksamee S., Khonthapagdee S. Privacy-Preserving Breast Density Classification in Mammograms Using Fuzzy C-Means and Homomorphic Encryption. 2025 17th International Conference on Knowledge and Smart Technology Kst 2025 (2025) , 376-381. 381. doi:10.1109/KST65016.2025.11003367 Retrieved from: https://hdl.handle.net/20.500.14740/21103
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Breast density is a significant risk factor for breast cancer, making its accurate assessment crucial for personalized screening strategies and improved patient outcomes. This study presents a privacy-preserving automated method for breast density classification using craniocaudal (CC) view mammograms from the VinDr-Mammo dataset. The approach combines Fuzzy C-Means clustering for tissue segmentation and morphological and texture feature extraction. Random Forest and XGBoost classifiers were trained on these features, achieving an accuracy of approximately 82 %. To address privacy concerns associated with sensitive medical data, the study employs Fully Homomorphic Encryption (FHE) via the Concrete-ml library. This allows encrypted models to perform secure computations directly on encrypted data, ensuring data privacy without sacrificing classification performance. Results demonstrate that privacy-preserving classifiers achieve accuracy comparable to traditional models, underscoring the feasibility of applying secure machine learning in clinical workflows. A practical workflow is also proposed to enable collaborative use between centralized hospitals and resource-limited health centers.