Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution

TL;DR

CPPDD addresses the challenge of scalable, privacy-preserving multi-party data sharing with verifiable all-or-nothing guarantees. It combines dual-layer affine obfuscation and sequential consensus locking to achieve unanimous-release confidentiality, while step and data checksums enable decentralized integrity verification and atomic aborts. The approach attains $O(N \cdot D)$ complexity, IND-CPA security under a non-colluding coordinator, and demonstrated scalability up to $N=500$ with sub-millisecond per-client computation and 100% deviation detection, outperforming MPC and HE baselines by orders of magnitude in FLOPs. This framework enables secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building in resource-constrained, regulated settings, offering a practical path to verifiable multi-party computation with minimal coordination overhead.

Abstract

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.

Figures (5)

• Figure 1: CPPDD System Architecture and Execution Flow. A vertical mapping of the protocol's eight cryptographic phases into four functional stages. The architecture transitions from a Trusted Setup (Coordinator) to a decentralized Sequential Unlocking chain (Clients), utilizing a Public Bulletin Board to enforce fairness. The design guarantees an all-or-nothing integrity model, triggering an atomic abort upon detecting any $f \ge 1$ malicious deviation.
• Figure 2: Scalability of CPPDD. Total execution time scales linearly with the number of clients $N$, while per-client computation remains constant and sub-millisecond.
• Figure 3:
• Figure 4: Visually faithful data recovery demonstration ($N=10$). Left: original MNIST digit; right: recovered digit via CPPDD.
• Figure :

Theorems & Definitions (6)

• Theorem 1: Correctness
• proof
• Theorem 2: CDIF Integrity and Fairness
• proof
• Theorem 3: IND-CPA Semantic Security of Masked Data
• proof