Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
Abstract
Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv)>= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
This paper introduces Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework and formalizes privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats.
A lightweight blockchain-based authentication framework for secure communication in Internet of Things (IoT) networks that integrates a permissioned blockchain with ECC-256 to provide mutual authentication, data integrity, and non-repudiation for resource-constrained IoT devices.
A. Abu-Ein, Obaida M. Al-hazaimeh· WSEAS Transactions on Inform...· 0 citations
Wireless Sensor Networks (WSN) are significant for various applications, however ensuring data security and energy consumption remains a critical challenge. The conventional methods lacked sufficient security, exhibited communication overhead, and energy inefficiencies. Therefore, this research proposes the Distributed Fractional Hawk Optimization (DtFHO) algorithm to address the limitations in cluster head selection for secure WSN routing. The integration of fractional theory improves the convergence speed and exploitation balance in cluster head selection. To secure the data routing, a blockchain network is employed, which maintains a transparent record of routing paths while preventing malicious node entries. Furthermore, the modified End-to-End Homomorphic encryption enables secure data sharing without decrypting sensitive information at intermediate nodes. Through considering the multimetric factors, the DtFHO algorithm offers a secure routing path, making it highly effective for large-scale and sensitive network scenarios. The DtFHO showcases a robust performance by achieving a minimum transaction time of 2.013 seconds, memory usage of 347.95 Kilobytes, Gas usage of 345.84 Kilobytes, encryption time of 2.012 seconds, and a maximum throughput ratio of 0.748, normalized energy of 0.766 Joules, with 153 alive nodes compared to the conventional methods.
Manish Agarwal, Aasheesh Shukla, V. Deolia· 2026 4th International Confe...· 0 citations
The findings indicate that unifying adaptive privacy preservation with decentralized integrity auditing yields a more complete cloud-security posture than either mechanism alone, and the paper outlines the empirical validation, including full-scale testbed experiments, required before deployment.
Jayakumar D, M. Ramamoorthy· International journal of com...· 0 citations
Smart cities increasingly depend on large-scale Internet of Things (IoT) infrastructures for traffic management, smart grids, and environmental monitoring. Ensuring data integrity, transparency, and privacy in such systems remains a major challenge because centralized platforms are vulnerable to manipulation, while conventional blockchain-based solutions suffer from scalability and confidentiality limitations. This study proposes TrustIoT-Chain, a privacy-preserving blockchain framework that integrates off-chain digital twins, cryptographic data commitments, zero-knowledge compliance verification, and a sharded blockchain architecture for scalable smart city monitoring. The objective of this work is to provide real-time verifiable IoT monitoring with strong privacy guarantees and high system throughput. Large-scale simulations with one million synthetic IoT events demonstrate that the proposed framework achieves up to 24,910 events/s throughput with an average verification latency of 410 ms using 16 shards. Energy consumption is reduced by approximately 45% compared with non-sharded blockchain systems with zeroknowledge proofs, while privacy leakage measured by mutual information decreases to 0.05 bits. The key novelty lies in the joint integration of the digital twins with the blockchain-based zero-knowledge auditing, and sharding for the smart city IoT systems. This approach enables transparent regulatory compliance verification without exposing the raw sensor data, offering the scalable, and privacy-aware foundation for the future smart city governance, and trusted IoT ecosystems.
Shrutika Khobragade, J. Bakal· 2026 7th International Confe...· 0 citations
The Internet of Vehicles (IoV) is evolving into a distributed electronic sensing and communication infrastructure in which vehicles, roadside units, and service platforms continuously exchange data for intelligent transportation services. However, cross-organization sharing of vehicle-borne sensing data can expose identity links, location traces, task routes, and raw sensor content. This paper proposes BAPPS, a Blockchain-Assisted Privacy-Preserving Sharing Scheme for Secure Data Exchange in the Internet of Vehicles. BAPPS combines anonymous identity issuance, zk-SNARK-based data-quality verification, elliptic-curve proxy re-encryption, and on-chain audit records. Data owners can prove that encrypted observations satisfy task-specific quality or access constraints without disclosing raw data, while a semi-honest service provider transforms ciphertexts only under authorization. The consortium blockchain records task publication, access verification, proof submission, and data-hash evidence, enabling traceable sharing without exposing plaintext observations. We further implement a Tendermint-style BFT consensus layer, denoted BAPPS-T, to reduce confirmation latency in the data-sharing workflow. Using the three available real Tendermint benchmark workbooks as repeated records, BAPPS-T achieved a mean consensus latency of 776.3 ms at 100 nodes (SD = 80.8 ms, n = 3, 95% CI = 575.5–977.1 ms), corresponding to a 90.17% latency reduction relative to Baseline 1. The results indicate that BAPPS can provide a reproducible protocol layer for trusted, privacy-aware sharing of mobile electronics observations under explicit deployment assumptions.
Lin Wang, Ke Chen, Fangxiao Li et al.· Electronics· 0 citations
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