TF-LB-CLPAS: Trapdoor-Free Lattice-Based Certificateless Public Auditing Scheme for Cloud-Assisted IoT

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Renuka Cheeturi, Syam Kumar Pasupuleti, Rashmi Ranjan Rout
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引用次数: 0

Abstract

Cloud-assisted IoT systems increasingly rely on certificateless public auditing (CLPA) mechanisms to ensure the integrity of outsourced data stored in the cloud while avoiding certificate management and key escrow issues. However, most existing CLPA schemes are built on conventional cryptographic assumptions and are therefore vulnerable to quantum attacks. To resist quantum adversaries, several lattice-based CLPA schemes (LB-CLPAS) have been proposed. Nevertheless, these schemes typically rely on complex lattice trapdoor constructions and adopt integer or ideal lattices, resulting in high computational overhead and rendering them impractical for resource-constrained IoT systems. To address these limitations, this paper proposes a trapdoor-free lattice-based certificateless public auditing scheme for cloud-assisted IoT (TF-LB-CLPAS). By eliminating complex trapdoors and adopting module lattices, the scheme significantly improves computational efficiency while maintaining strong post-quantum security. Security is formally proven under the Module-SIS and Module-LWE assumptions. Performance evaluations demonstrate lower computational overhead and superior practicality compared to existing trapdoor-based LB-CLPAS, making it well suited for cloud-assisted IoT devices.

TF-LB-CLPAS:云辅助物联网无trapdoor - based lattice无证书公共审计方案
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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