Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 61 references
TL;DR
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.
Abstract
Federated learning relies on a central server to aggregate client updates, making secure aggregation (SA) essential for privacy protection. Masking-based SA schemes dominate large-scale deployments due to their low overhead, yet they face a fundamental tension: efficient schemes assume a semi-honest server and provide no client-side cryptographic verification, while existing verifiable schemes admit algebraic degrees of freedom that allow a malicious server to forge results that pass all client-side checks. We present TriVer, a lightweight SA protocol that resolves this tension by augmenting a single-mask HPRF-based framework with a triple-binding verification mechanism grounded in elliptic curve cryptography. Each client independently verifies the aggregated result through three non-interactive checks: threshold signature verification, commitment aggregation consistency, and model-hash binding. Passing all three checks simultaneously is computationally equivalent to solving the elliptic curve discrete logarithm problem (ECDLP). We prove 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. Experimentally, TriVer achieves a per-round latency of 1.96 s, approximately six times lower than LightVeriFL (11.68 s) and roughly twice the cost of Aion (0.97 s), with no forged result accepted across all evaluated attack types in our simulations, including the algebraic bypass attack; under the same attack, LightVeriFL’s single-constraint verification equation did not prevent forgery in our evaluated scenario. TriVer tolerates client dropout rates up to 40% with negligible recovery overhead, whereas LightVeriFL fails to complete aggregation at a 20% dropout rate.
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
Federated learning alleviates data silos through a “data-local, model-global” paradigm, but transmitting plaintext gradients exposes clients to reconstruction attacks from malicious servers. Existing secure aggregation methods face trade-offs among privacy, accuracy, and efficiency: homomorphic encryption incurs high overhead, differential privacy sacrifices accuracy, and lightweight secret-sharing schemes often lack weighted aggregation support and suffer accuracy degradation as client numbers grow. To address these limitations, we propose SecAGG, a lossless secure weighted aggregation scheme based on additive secret sharing. SecAGG adopts a three-tier architecture consisting of client clusters, cooperative servers, and a super server. Clients split weighted model parameters into random shares and distribute them to cooperative servers, which perform encrypted partial aggregation before the super server securely reconstructs the global model. Experimental results demonstrate that SecAGG achieves strict security against up to M-1 colluding servers under the semi-honest model while preserving FedAvg-equivalent accuracy with minimal computation and communication overhead, effectively balancing privacy, accuracy, and efficiency.
Xiaomei Tian· 2026 3rd World Conference on...· 0 citations
An automated formal verification study of the Secure Aggregation protocol using ProVerif is presented, demonstrating how automated formal verification can support trustworthy and verifiable federated learning software systems.
Yuping Yan, Zi-Cen Zhao, Ming Fei et al.· International Conference on...· 0 citations
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.
Jianhong Zhang, Miao Xue, Xinyu Bai· IEEE Internet of Things Jour...· 0 citations
Paras, the first two-server protocol for private histogram computation that achieves robustness against collusion between a malicious server and arbitrarily many malicious clients is presented, and is shown to be highly efficient and scalable.
Dimitris Mouris, Lucas Piske, Pratik Sarkar et al.· IACR Cryptology ePrint Archi...· 0 citations
Federated learning enables collaborative model training between central servers and distributed clients without collecting users' raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the high sensitivity of local training data and model gradient parameters in federated learning, protecting identity privacy and interaction security has become extremely critical. Therefore, mutual identity authentication is indispensable to restrict illegal client access and prevent malicious parameter transmission and data tampering. In this paper, we propose a lightweight anonymous authentication scheme for federated learning (FedLAS), which realizes secure mutual authentication between servers and clients and establishes a shared session key for subsequent encrypted interaction. In particular, the proposed scheme eliminates the reliance on high-cost cryptographic operations such as bilinear pairing, thus minimizing computational and communication overhead. Furthermore, informal security analysis demonstrates that our FedLAS scheme can resist multiple common attacks and meet predefined security requirements. Extensive comparative experiments show that the FedLAS scheme achieves excellent performance in computational and communication cost. It is well suitable for resource-constrained federated learning scenarios.
Shu Wu, Guoqiang Meng, Linlin Lu et al.· Scientific Reports· 0 citations
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