In blockchain-based medical data sharing systems, sensitive medical data such as electronic health records and diagnostic reports require secure sharing with fine-grained access control and accountable usage. Existing attribute-based encryption (ABE) schemes lack mechanisms for auditing data usage after decryption, leaving data misuse undetectable and unaccountable. To address this limitation, this paper proposes Commit-HABE, a commitment-based auditable ABE scheme. Users submit their attribute sets along with signed usage commitments prior to data access, which are immutably recorded on a consortium blockchain. In addition, a traceable watermark cryptographically linked to each commitment can be embedded into the decrypted data, enabling post-hoc leakage detection and attribution. Security analysis indicates that Commit-HABE achieves data confidentiality, commitment non-repudiation, and traceability. We implement Commit-HABE alongside three representative CP-ABE schemes and conduct a performance evaluation. Experimental results demonstrate that Commit-HABE reduces encryption time by up to 29.5% compared to existing schemes and maintains comparable decryption performance. The commitment overhead is negligible $(\approx 0.0016 \text{ms})$, watermark embedding scales linearly from 0.28 ms to 6.10 ms, and the blockchain system sustains 100% success under up to 100 queries per second.
Ke-Fei Li, Xiangwei Meng, Cangming Liang et al.· Fall Joint Computer Conferen...· 0 citations
This work proposes HFL-Lite, a hierarchical federated learning framework that achieves practical privacy without cryptographic primitives, and shows that HFL-Lite reduces per-round communication to 0.5 KB and sensor-side latency to 9 ms while delivering competitive accuracy.
Cangming Liang, Kuan Ching Li, Zulong Diao et al.· Tsinghua Science and Technol...· 0 citations
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