A Privacy-Preserving Data Sharing Framework Driven by Blockchain for Smart Childcare Subsidy Systems
DOI:
https://doi.org/10.4108/eetsis.13557Keywords:
Smart civil affairs, childcare subsidy, blockchain, privacy protection, zero-knowledge proof, attribute-based encryption, smart contract, audit traceabilityAbstract
With the continuing development of digital government and public childcare-assistance programs, cross-departmental subsidy services must jointly address sensitive-data minimization, dynamic authorization, fraudulent-application identification, and accountable auditing. This paper proposes a blockchain-driven privacy-preserving data-sharing framework that combines a consortium blockchain, on-chain evidence with off-chain ciphertext storage, zero-knowledge eligibility proofs, attribute-based encryption, proxy re-encryption, secure aggregation, federated learning, and smart-contract auditing. The contribution is a scenario-specific system integration and governance design rather than a new cryptographic primitive. In addition to the original Hyperledger Fabric feasibility test, the revision adds literature-grounded validation against peer-reviewed studies of childcare-subsidy administration and administrative-data evaluation, component implementation-status disclosure, a 2048-bit proof-of-knowledge precomputation microbenchmark, non-IID federated-learning tests, and colluding-node poisoning tests. The original prototype reaches 718 TPS and 236 ms average latency at 400 concurrent requests, 91.8% federated-learning accuracy, and 138 ms on-chain index retrieval for 20,000 audit records. In the added five-run tests, strong non-IID data retain an F1 score of 82.69% +/- 0.09%; adaptive clipping and robust aggregation retain F1 scores of 84.04% and 84.14% under one and two colluding sign-flip clients, respectively. Offline commitment precomputation reduces the online arithmetic time for four eligibility subproofs from 8.03 +/- 0.34 ms to 0.06 +/- 0.01 ms. These results support the feasibility of the integrated framework while clarifying that production deployment still requires authorized local data, audited cryptographic libraries, and multi-orderer field validation.
References
[1] Jenkins J M, Nguyen T. Keeping Kids in Care: Reducing Administrative Burden in State Child Care Development Fund Policy[J]. Journal of Public Administration Research and Theory, 2022, 32(1): 23-40. DOI: 10.1093/jopart/muab020.
[2] Morrissey T W, Heflin C M, Fannin W C. Room to grow: examining participation and stability in child care subsidies using state administrative data[J]. Early Childhood Research Quarterly, 2023, 62: 150-162. DOI: 10.1016/j.ecresq.2022.08.003.
[3] Horak S, Ward C. Evaluating a state child care assistance program using administrative data[J]. Evaluation and Program Planning, 2022, 92: 102094. DOI: 10.1016/j.evalprogplan.2022.102094.
[4] Casino F, Dasaklis T K, Patsakis C. A systematic literature review of blockchain-based applications: Current status, classification and open issues[J]. Telematics and Informatics, 2019, 36: 55-81. DOI: 10.1016/j.tele.2018.11.006.
[5] Monrat A A, Schelén O, Andersson K. A survey of blockchain from the perspectives of applications, challenges, and opportunities[J]. IEEE Access, 2019, 7: 117134-117151. DOI: 10.1109/ACCESS.2019.2936094.
[6] Zheng Z, Xie S, Dai H N, Chen X, Wang H. Blockchain challenges and opportunities: A survey[J]. International Journal of Web and Grid Services, 2018, 14(4): 352-375. DOI: 10.1504/IJWGS.2018.095647.
[7] Ølnes S, Ubacht J, Janssen M. Blockchain in government: Benefits and implications of distributed ledger technology for information sharing[J]. Government Information Quarterly, 2017, 34(3): 355-364. DOI: 10.1016/j.giq.2017.09.007.
[8] Elisa N, Yang L, Chao F, Cao Y. A framework of blockchain-based secure and privacy-preserving e-government system[J]. Wireless Networks, 2023, 29(3): 1005-1015. DOI: 10.1007/s11276-018-1883-0.
[9] Zhang Y, Kasahara S, Shen Y, Jiang X, Wan J. Smart contract-based access control for the Internet of Things[J]. IEEE Internet of Things Journal, 2019, 6(2): 1594-1605. DOI: 10.1109/JIOT.2018.2847705.
[10] Batubara F R, Ubacht J, Janssen M. Challenges of blockchain technology adoption for e-government: A systematic literature review[C]//Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age. New York: ACM, 2018: Article 76, 9 pages. DOI: 10.1145/3209281.3209317.
[11] Tan E, Mahula S, Crompvoets J. Blockchain governance in the public sector: A conceptual framework for public management[J]. Government Information Quarterly, 2022, 39(1): 101625. DOI: 10.1016/j.giq.2021.101625.
[12] Janssen M, Weerakkody V, Ismagilova E, Sivarajah U, Irani Z. A framework for analysing blockchain technology adoption: Integrating institutional, market and technical factors[J]. International Journal of Information Management, 2020, 50: 302-309. DOI: 10.1016/j.ijinfomgt.2019.08.012.
[13] Piao C, Hao Y, Yan J, Jiang X. Privacy preserving in blockchain-based government data sharing: A Service-On-Chain (SOC) approach[J]. Information Processing & Management, 2021, 58(5): 102651. DOI: 10.1016/j.ipm.2021.102651.
[14] Elisa N, Yang L, Chao F, Naik N, Boongoen T. A secure and privacy-preserving e-government framework using blockchain and artificial immunity[J]. IEEE Access, 2023, 11: 8773-8789. DOI: 10.1109/ACCESS.2023.3239814.
[15] Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B. A survey on IoT security: Application areas, security threats, and solution architectures[J]. IEEE Access, 2019, 7: 82721-82743. DOI: 10.1109/ACCESS.2019.2924045.
[16] Dwivedi A D, Srivastava G, Dhar S, Singh R. A decentralized privacy-preserving healthcare blockchain for IoT[J]. Sensors, 2019, 19(2): 326. DOI: 10.3390/s19020326.
[17] Hasselgren A, Kralevska K, Gligoroski D, Pedersen S A, Faxvaag A. Blockchain in healthcare and health sciences—A scoping review[J]. International Journal of Medical Informatics, 2020, 134: 104040. DOI: 10.1016/j.ijmedinf.2019.104040.
[18] McMahan B, Moore E, Ramage D, Hampson S, Agüera y Arcas B. Communication-efficient learning of deep networks from decentralized data[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, 2017, 54: 1273-1282.
[19] Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 12:1-12:19. DOI: 10.1145/3298981.
[20] Kairouz P, McMahan H B, Avent B, Bellet A, Bennis M, Bhagoji A N, Bonawitz K, Charles Z, Cormode G, Cummings R, et al. Advances and open problems in federated learning[J]. Foundations and Trends in Machine Learning, 2021, 14(1-2): 1-210. DOI: 10.1561/2200000083.
[21] Li T, Sahu A K, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50-60. DOI: 10.1109/MSP.2020.2975749.
[22] Mothukuri V, Parizi R M, Pouriyeh S, Huang Y, Dehghantanha A, Srivastava G. A survey on security and privacy of federated learning[J]. Future Generation Computer Systems, 2021, 115: 619-640. DOI: 10.1016/j.future.2020.10.007.
[23] Liu K, Yan Z, Liang X, Kantola R, Hu C. A survey on blockchain-enabled federated learning and its prospects with digital twin[J]. Digital Communications and Networks, 2024, 10(2): 248-264. DOI: 10.1016/j.dcan.2022.08.001.
[24] Wu L, Ruan W, Hu J, He Y. A survey on blockchain-based federated learning[J]. Future Internet, 2023, 15(12): 400. DOI: 10.3390/fi15120400.
[25] Hu K, Gong S, Zhang Q, Seng C, Xia M, Jiang S. An overview of implementing security and privacy in federated learning[J]. Artificial Intelligence Review, 2024, 57: 204. DOI: 10.1007/s10462-024-10846-8.
[26] Guembe B, Misra S, Azeta A. Privacy issues, attacks, countermeasures and open problems in federated learning: A survey[J]. Applied Artificial Intelligence, 2024, 38(1): 2410504. DOI: 10.1080/08839514.2024.2410504.
[27] Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan H B, Patel S, Ramage D, Segal A, Seth K. Practical secure aggregation for privacy-preserving machine learning[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 1175-1191. DOI: 10.1145/3133956.3133982.
[28] Guo Z, Sun Y, Zhang X, Wu L. An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance[J]. EAI Endorsed Transactions on Scalable Information Systems, 2026, 12(8). DOI: 10.4108/eetsis.11991.
[29] Li Q, Wen Z, He B. Practical federated gradient boosting decision trees[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 4642-4649. DOI: 10.1609/aaai.v34i04.5895.
[30] Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y. A survey on federated learning[J]. Knowledge-Based Systems, 2021, 216: 106775. DOI: 10.1016/j.knosys.2021.106775.
[31] Sahai A, Waters B. Fuzzy identity-based encryption[C]//Advances in Cryptology-EUROCRYPT 2005. Lecture Notes in Computer Science, vol. 3494. Berlin: Springer, 2005: 457-473. DOI: 10.1007/11426639_27.
[32] Bethencourt J, Sahai A, Waters B. Ciphertext-policy attribute-based encryption[C]//Proceedings of the 2007 IEEE Symposium on Security and Privacy. Oakland: IEEE, 2007: 321-334. DOI: 10.1109/SP.2007.11.
[33] Lewko A, Waters B. Decentralizing attribute-based encryption[C]//Advances in Cryptology-EUROCRYPT 2011. Lecture Notes in Computer Science, vol. 6632. Berlin: Springer, 2011: 568-588. DOI: 10.1007/978-3-642-20465-4_31.
[34] Ateniese G, Fu K, Green M, Hohenberger S. Improved proxy re-encryption schemes with applications to secure distributed storage[J]. ACM Transactions on Information and System Security, 2006, 9(1): 1-30. DOI: 10.1145/1127345.1127346.
[35] Ben-Sasson E, Chiesa A, Garman C, Green M, Miers I, Tromer E, Virza M. Zerocash: Decentralized anonymous payments from Bitcoin[C]//Proceedings of the 2014 IEEE Symposium on Security and Privacy. San Jose: IEEE, 2014: 459-474. DOI: 10.1109/SP.2014.36.
[36] Goldwasser S, Micali S, Rackoff C. The knowledge complexity of interactive proof systems[J]. SIAM Journal on Computing, 1989, 18(1): 186-208. DOI: 10.1137/0218012.
[37] Gennaro R, Gentry C, Parno B, Raykova M. Quadratic span programs and succinct NIZKs without PCPs[C]//Advances in Cryptology-EUROCRYPT 2013. Lecture Notes in Computer Science, vol. 7881. Berlin: Springer, 2013: 626-645. DOI: 10.1007/978-3-642-38348-9_37.
[38] Paillier P. Public-key cryptosystems based on composite degree residuosity classes[C]//Advances in Cryptology-EUROCRYPT 1999. Lecture Notes in Computer Science, vol. 1592. Berlin: Springer, 1999: 223-238. DOI: 10.1007/3-540-48910-X_16.
[39] Gentry C. Fully homomorphic encryption using ideal lattices[C]//Proceedings of the 41st Annual ACM Symposium on Theory of Computing. New York: ACM, 2009: 169-178. DOI: 10.1145/1536414.1536440.
[40] Boneh D, Boyen X, Shacham H. Short group signatures[C]//Advances in Cryptology-CRYPTO 2004. Lecture Notes in Computer Science, vol. 3152. Berlin: Springer, 2004: 41-55. DOI: 10.1007/978-3-540-28628-8_3.
[41] Song D X, Wagner D, Perrig A. Practical techniques for searches on encrypted data[C]//Proceedings of the 2000 IEEE Symposium on Security and Privacy. Berkeley, CA, USA: IEEE Computer Society, 2000: 44-55. DOI: 10.1109/SECPRI.2000.848445.
[42] Dwork C. Differential privacy[C]//Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, Part II. Lecture Notes in Computer Science, vol. 4052. Berlin: Springer, 2006: 1-12. DOI: 10.1007/11787006_1.
[43] Dwork C, Roth A. The algorithmic foundations of differential privacy[J]. Foundations and Trends in Theoretical Computer Science, 2014, 9(3-4): 211-407. DOI: 10.1561/0400000042.
[44] Chenthara S, Ahmed K, Whittaker F. Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment[J]. EAI Endorsed Transactions on Scalable Information Systems, 2019, 6(22): e6. DOI: 10.4108/eai.13-7-2018.159356.
[45] Schnorr C P. Efficient identification and signatures for smart cards[C]//Advances in Cryptology-CRYPTO '89. Lecture Notes in Computer Science, vol. 435. Berlin: Springer, 1990: 239-252. DOI: 10.1007/0-387-34805-0_22.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ruiyang Li, Xiaomei Liu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
