This paper presents a secure data sharing platform that organises KR-IBI, KR-IBE, KR-PEKS, and KR-PAEKS into an end-to-end Rust/Tauri workflow for registration, authentication, encrypted upload, searchable retrieval, and authorised decryption. The work addresses a deployment-level composition problem rather than proposing a new primitive: practical data sharing requires coordinated credential handling, payload representation, searchable indexing, session control, and record integrity across schemes with distinct interfaces. The platform supports text, file, and image payloads through a hybrid KR-IBE/HKDF-SHA-256/AES-256-GCM layer. Fresh KR-IBE key material is generated by uniformly sampling a nonzero scalar and multiplying the Ed25519 prime-order subgroup generator, providing approximately 252 bits of min-entropy before HKDF derivation. An evaluation with 100 repetitions per configuration over Enron-derived workloads containing 100–10,000 records and 1, 5, 10, or 20 authorised identities achieved 100/100 correctness for authorised retrieval and decryption, wrong-keyword and wrong-scheme rejection, and unauthorised-access rejection. KR-PEKS search latency ranged from 29.26 ms at 100 records to 3023.82 ms at 10,000 records, whereas KR-PAEKS ranged from 775.53 ms to 93,045.52 ms. These results quantify the performance distinction between the lower-latency KR-PEKS mode and the sender-authenticated searchable encryption provided by KR-PAEKS.
Federated learning (FL) enables collaborative medical image analysis without centralising sensitive data, making it highly suitable for privacy-critical applications such as brain tumour detection from magnetic resonance imaging (MRI). However, conventional FL frameworks remain vulnerable to parameter-level information leakage through shared model updates, which may expose sensitive information about the underlying medical data. This study proposes a privacy-preserving FL framework that integrates Cheon-Kim-Kim-Song (CKKS)-based homomorphic encryption (HE) for secure aggregation with an Xception convolutional neural network (CNN) for multi-class brain tumour classification. In the proposed framework, local models are trained in plaintext on distributed clients, while the resulting model parameters are encrypted before transmission and aggregation. This approach ensures that the central server performs aggregation without gaining access to plaintext model updates, thereby improving confidentiality and reducing the risk of privacy breaches. Experiments conducted on a four-class brain MRI dataset demonstrate that the proposed approach achieves a global classification accuracy of 97.87%, with less than 1% performance degradation compared to conventional non-encrypted FL. Furthermore, a comprehensive analysis of encryption, decryption, and federated communication round latency is performed to evaluate the computational overhead introduced by CKKS-based HE. The experimental findings confirm the practical feasibility of integrating HE-assisted secure aggregation with deep learning-based FL systems for privacy-preserving medical imaging applications, while maintaining high diagnostic performance and reliable collaborative model training.
Anik Sen, Swee-Huay Heng, Shing-Chiang Tan· International Conference on...· 0 citations
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