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.

翻译：我们提出基于共识的隐私保护数据分发（CPPDD）框架，这是一种轻量级且设置后自主化的协议，用于安全的多客户端数据聚合。该框架通过双层保护机制实现全票通过的机密性：该机制结合了每客户端仿射掩码与优先级驱动的顺序共识锁定。通过步骤校验和（σ_S）与数据校验和（σ_D）验证去中心化完整性，支持自主恶意偏差检测与原子性中止，无需持续协调。该设计支持标量、向量和矩阵类型的数据负载，具有O(N*D)的计算与通信复杂度，可选边缘服务器卸载，并在N-1个客户端腐败场景下抵抗合谋攻击。形式化分析证明了正确性、共识依赖的完整性与公平性（CDIF）——偏差发生时以压倒性概率中止，以及基于伪随机函数族的IND-CPA安全性。基于MNIST数据派生向量的实验评估表明，该框架在N=500时具有线性可扩展性，每个客户端的计算时间低于毫秒级。与MPC和HE基线方法相比，该框架实现了100%的恶意偏差检测率、精确的数据恢复能力，且FLOPs降低了三至四个数量级。CPPDD可在安全投票、联盟联邦学习、区块链托管及地理信息能力建设等场景中支持原子性协作，有效解决了受监管及资源受限环境中可扩展性、信任最小化与可验证多方计算之间的关键差距。