一种可验证的轻量级多客户端数据共享框架，支持高效安全协作。
Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
- 通过双重掩码与优先级共识锁实现无条件保密
- 500个客户端下每客户端计算低于1毫秒，误检率为0
- 适合高监管、资源受限场景的可信协作
我们提出了一种基于共识的隐私保护数据分发（CPPDD）框架，一种轻量级且无需预设的自主协议，用于安全的多客户端数据聚合。该框架通过双层保护机制——客户端级仿射掩码与优先级驱动的顺序共识锁定——实现一致发布下的保密性。去中心化完整性通过步骤（sigma_S）和数据（sigma_D）校验和验证，支持自主检测恶意偏离并原子中止，无需持续协调。设计支持标量、向量和矩阵数据，计算与通信复杂度为O(N*D)，可选边缘服务器卸载，并在最多N-1个节点被攻破时仍具备抗合谋能力。形式化分析证明其正确性、共识依赖完整性与公平性（CDIF），偏离时以高概率触发中止，且在伪随机函数族假设下满足IND-CPA安全。实测在MNIST衍生向量上实现线性扩展，至N=500时每客户端计算时间不足1毫秒。框架实现100%恶意偏离检测、精确数据恢复，且相比MPC与HE基线降低三到四个数量级的浮点运算量。该框架适用于安全投票、联盟联邦学习、区块链托管及地理信息能力建设，解决了受监管和资源受限环境下可扩展性、信任最小化与可验证多方计算的关键缺口。
原文摘要 · Abstract (English)
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality through a dual-layer protection mechanism that combines per-client affine masking with priority-driven sequential consensus locking. Decentralized integrity is verified via step (sigma_S) and data (sigma_D) checksums, facilitating autonomous malicious deviation detection and atomic abort without requiring persistent coordination. The design supports scalar, vector, and matrix payloads with O(N*D) computation and communication complexity, optional edge-server offloading, and resistance to collusion under N-1 corruptions. Formal analysis proves correctness, Consensus-Dependent Integrity and Fairness (CDIF) with overwhelming-probability abort on deviation, and IND-CPA security assuming a pseudorandom function family. Empirical evaluations on MNIST-derived vectors demonstrate linear scalability up to N = 500 with sub-millisecond per-client computation times. The framework achieves 100% malicious deviation detection, exact data recovery, and three-to-four orders of magnitude lower FLOPs compared to MPC and HE baselines. CPPDD enables atomic collaboration in secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building, addressing critical gaps in scalability, trust minimization, and verifiable multi-party computation for regulated and resource-constrained environments.
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