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：我们提出了基于共识的隐私保护数据分发（CPPDD）框架，这是一种轻量级且支持后置自主配置的安全多客户端数据聚合协议。该框架通过双层保护机制——结合了逐客户端仿射掩码与优先级驱动的顺序共识锁定——实现了全票释放保密性。去中心化的完整性通过步骤校验和（σ_S）与数据校验和（σ_D）进行验证，支持自主恶意偏差检测与原子化中止，无需持续协调。该设计支持标量、向量及矩阵负载，具有O(N*D)的计算与通信复杂度，支持可选的边缘服务器卸载，并在N-1个节点被腐化时具备抗共谋能力。形式化分析证明了其正确性、共识依赖的完整性与公平性（CDIF），以及在发生偏差时以压倒性概率中止的特性，并在假设存在伪随机函数族的前提下实现了IND-CPA安全性。在MNIST衍生的向量数据上进行的实证评估表明，该框架在客户端规模达到N=500时仍保持线性可扩展性，单客户端计算时间低于毫秒级。与多方计算（MPC）和同态加密（HE）基线相比，CPPDD实现了100%的恶意偏差检测率、精确数据恢复能力，以及降低三到四个数量级的浮点运算量。CPPDD可支持安全投票、联盟联邦学习、区块链托管及地理信息能力建设等场景中的原子化协作，为受监管和资源受限环境下的可扩展性、信任最小化与可验证多方计算等关键需求提供了解决方案。