Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 40787-40798· 0 citations· 40 references
Abstract
The consumer Internet of Things (CIoT) enables large-scale sensing and data-driven services by connecting billions of devices. However, the privacy-preserving data aggregation in mobile edge computing (MEC)-enabled CIoT remains challenging when the edge aggregator is malicious, rather than simply honest-but-curious. Specifically, a malicious MEC server can selectively drop user reports or tamper with ciphertexts, undermining data integrity and ownership. To address this, we propose a verifiable and fault-tolerant privacy-preserving aggregation scheme. By substituting computationally intensive homomorphic encryption (HE) with a double-masking mechanism and integrating bilinear pairing-based accumulators, the proposed scheme provides resilience against tampering and deletion attacks while ensuring data privacy. The design supports user dropouts within a single reporting round without requiring interactive recovery, enabling each device to locally audit the inclusion of the corresponding ciphertext via a compact membership witness. Theoretical analysis and experimental results demonstrate that the proposed scheme reduces communication overhead and payload size. It satisfies the security requirements of CIoT ecosystems and offers a practical tradeoff between security and efficiency for latency-sensitive deployments.
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.
Cloud computing offers organizations scalable storage and computation, but outsourcing data processing to third-party infrastructure introduces serious privacy and confidentiality risks. Two complementary paradigms have emerged to address this challenge: homomorphic encryption (HE), which allows computation directly on encrypted data, and federated learning (FL), which enables collaborative model training without centralizing raw data. This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination. We propose a taxonomy of existing approaches, synthesize representative literature in a comparative table, illustrate a generic hybrid HE-FL architecture, and evaluate the two paradigms against criteria including data exposure, computational overhead, communication cost, resistance to inference attacks, and cloud deployment readiness. We further identify open challenges — including computational latency, key management, non-IID data distributions, and standardization gaps — and outline promising directions for future research, such as hardware-accelerated HE, adaptive encryption granularity, and standardized hybrid privacy frameworks for cloud-native machine learning.
Shivendra Shukla, Chandra Shekhar Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
A flexible privacy-preserving framework that combines the scalability of broadcast encryption with the fine-grained access control of Attribute-Based Encryption through a novel pseudo-layer encryption model, and achieves confidentiality, forward and backward secrecy, and collusion resistance.
Seyyed Mohammad Safi, Mahnaz Rafie, Sarina Sadat Mirmohammadi· Journal of Supercomputing· 0 citations
As the cloud computing and mass data sharing develop, data integrity and privacy has become an imperativeissue. Conventional remote data auditing techniques tend to reveal sensitive data or they have high computational cost.In order to overcome these shortcomings, the Fully Homomorphic Encryption enhanced Remote Method Invocation(FHEbRMI) mechanism that includes a combination of the Modified Least Squares (MLS) optimization model and theproposed cloud auditing security and efficiency are proposed in this paper. The suggested system provides an encrypteddata auditing system, which involves RMI-based communication, to enable the client, server, and third-party auditor toperform their verification functions remotely without the disclosure of the plaintext data. An actual execution of thesuggested structure is introduced, such as secure key generation, trapdoor-based dimensionality reduction, ciphertextmultiplication, and optimized homomorphic functions. Moreover, the RMI interface provides a smooth communicationamong the distributed nodes and increases the scalability and minimizes transmission delays. A comparative study withthe recent homomorphic-based auditing schemes like blockchain-assisted, certificateless and lattice-based FHE modelreveals that the proposed FHEbRMI-MLS model has better performance in terms of encryption/decryption latency,computational cost, and encryption overhead. The experimental performance is indicative of an average 37 and 42factor in speed of encryption and enhancement of computational efficiency respectively with respect to the traditionalFHE models. This paper presents a viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency.
Deepshikha Chaturvedi, Vidyullata Devmane, Shashikant S. Radke et al.· International Journal of Com...· 0 citations
It is proved that TriVer satisfies client data privacy, aggregation correctness, and aggregation-result non-forgeability in the Random Oracle Model under ECDLP hardness, HPRF pseudorandomness, and hash collision resistance, against a fully malicious server that may collude with a subset of aggregators and clients.
Guangye Zhu, Liqiang Wu, Weidong Du· Journal of King Saud Univers...· 0 citations
This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.
Harsh Kasyap, Anil Kumar Pradhan, U. Atmaca et al.· 0 citations
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