HRL-D3: High resolution and lightweight defective data detection for IoT data integrity

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-08-26 DOI:10.1016/j.future.2025.108089

Suparna Kar, Kaif Ali Khan P, Ravi Surendra Nalawade, Vanga Aravind Shounik, Vikas Ravi Patil, Kotaro Kataoka

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引用次数: 0

Abstract

Due to the limited storage capacity of Internet of Things (IoT) devices, the use of third-party cloud storage service is an integral part of IoT based systems. Ensuring data integrity in cloud storage services is paramount for maintaining the safety and trustworthiness of the data generated and consumed by IoT applications. While verifying data integrity through defective data detection, the number of False Positives and False Negatives should be fewer so that the resolution is higher. However, increasing the resolution also incurs an increase in metadata for integrity verification and results in higher storage overhead. This paper proposes High Resolution and Lightweight Defective Data Detection (HRL-D3) for IoT data integrity with a short verification time, low storage overhead and minimal computational cost. HRL-D3 introduces 1) the use of Merkle Hash Tree and the novel concept of Intermediate Hash for enabling faster Data Integrity Verification (DIV) and higher resolution, and 2) an Adaptive Data Chunking Algorithm for balancing the trade-off between resolution and storage overhead. Our security analysis examined the risks of potential attacks to HRL-D3, and outlined the prevention provided by the proposed solution as well as the mitigation through an operational workaround. A Proof of Concept implementation HRL-D3 was evaluated and demonstrated its effectiveness in balancing the trade off between the resolution and the storage overhead tradeoff as well as achieving low-latency DIV.

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HRL-D3：用于物联网数据完整性的高分辨率和轻量级缺陷数据检测

由于物联网（IoT）设备的存储容量有限，使用第三方云存储服务是基于物联网的系统不可缺少的一部分。确保云存储服务中的数据完整性对于维护物联网应用生成和使用的数据的安全性和可信度至关重要。在通过缺陷数据检测验证数据完整性的同时，应减少假阳性和假阴性的数量，从而提高分辨率。但是，提高分辨率也会增加用于完整性验证的元数据，并导致更高的存储开销。本文提出了一种用于物联网数据完整性的高分辨率和轻量级缺陷数据检测（HRL-D3），具有验证时间短、存储开销低和计算成本最低的特点。HRL-D3引入了1)使用Merkle哈希树和中间哈希的新概念，以实现更快的数据完整性验证（DIV）和更高的分辨率，以及2)自适应数据分块算法，以平衡分辨率和存储开销之间的权衡。我们的安全分析检查了针对HRL-D3的潜在攻击风险，并概述了拟议解决方案提供的预防措施以及通过操作解决方案缓解的措施。对概念验证实现HRL-D3进行了评估，并证明了其在平衡分辨率和存储开销之间的权衡以及实现低延迟DIV方面的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.