High-Confidence Computing最新文献_第6页

Trusted access control mechanism for data with blockchain-assisted attribute encryption 区块链辅助属性加密数据可信访问控制机制

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-08-31 DOI: 10.1016/j.hcc.2024.100265

Chang Liu , Dong Wang , Da Li , Shaoyong Guo , Wenjing Li , Xuesong Qiu

{"title":"Trusted access control mechanism for data with blockchain-assisted attribute encryption","authors":"Chang Liu , Dong Wang , Da Li , Shaoyong Guo , Wenjing Li , Xuesong Qiu","doi":"10.1016/j.hcc.2024.100265","DOIUrl":"10.1016/j.hcc.2024.100265","url":null,"abstract":"<div><div>In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100265"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task migration with deadlines using machine learning-based dwell time prediction in vehicular micro clouds 基于机器学习的车辆微云停留时间预测的任务迁移

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2025-03-03 DOI: 10.1016/j.hcc.2025.100314

Ziqi Zhou , Agon Memedi , Chunghan Lee , Seyhan Ucar , Onur Altintas , Falko Dressler

{"title":"Task migration with deadlines using machine learning-based dwell time prediction in vehicular micro clouds","authors":"Ziqi Zhou , Agon Memedi , Chunghan Lee , Seyhan Ucar , Onur Altintas , Falko Dressler","doi":"10.1016/j.hcc.2025.100314","DOIUrl":"10.1016/j.hcc.2025.100314","url":null,"abstract":"<div><div>Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks. In this context, the concept of vehicular micro clouds (VMCs) has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks. As many tasks in this application domain are time critical, offloading to the cloud is prohibitive. Additionally, task deadlines have to be dealt with. This paper addresses two main challenges. First, we present a task migration algorithm supporting deadlines in vehicular edge computing. The algorithm is following the earliest deadline first model but in presence of dynamic processing resources, <em>i.e</em>, vehicles joining and leaving a VMC. This task offloading is very sensitive to the mobility of vehicles in a VMC, <em>i.e</em>, the so-called dwell time a vehicles spends in the VMC. Thus, secondly, we propose a machine learning-based solution for dwell time prediction. Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC. Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya. Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions, advancing the state of the art in vehicular edge computing.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100314"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep reinforcement learning based resource provisioning for federated edge learning 基于深度强化学习的联邦边缘学习资源配置

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-08-30 DOI: 10.1016/j.hcc.2024.100264

Xingyun Chen, Junjie Pang, Tonghui Sun

{"title":"Deep reinforcement learning based resource provisioning for federated edge learning","authors":"Xingyun Chen, Junjie Pang, Tonghui Sun","doi":"10.1016/j.hcc.2024.100264","DOIUrl":"10.1016/j.hcc.2024.100264","url":null,"abstract":"<div><div>With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100264"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review 大型语言模型（LLM）及其代理的数据隐私保护：文献综述

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2025-02-28 DOI: 10.1016/j.hcc.2025.100300

Biwei Yan , Kun Li , Minghui Xu , Yueyan Dong , Yue Zhang , Zhaochun Ren , Xiuzhen Cheng

{"title":"On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review","authors":"Biwei Yan , Kun Li , Minghui Xu , Yueyan Dong , Yue Zhang , Zhaochun Ren , Xiuzhen Cheng","doi":"10.1016/j.hcc.2025.100300","DOIUrl":"10.1016/j.hcc.2025.100300","url":null,"abstract":"<div><div>Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100300"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure and trusted sharing mechanism of private data for Internet of Things 安全可信的物联网私有数据共享机制

IF 3.2

High-Confidence Computing Pub Date : 2025-06-01 Epub Date: 2024-12-24 DOI: 10.1016/j.hcc.2024.100273

Mengyuan Li , Shaoyong Guo , Wenjing Li , Ao Xiong , Xiaoming Zhou , Jun Qi , Feng Qi , Dong Wang , Da Li

{"title":"Secure and trusted sharing mechanism of private data for Internet of Things","authors":"Mengyuan Li , Shaoyong Guo , Wenjing Li , Ao Xiong , Xiaoming Zhou , Jun Qi , Feng Qi , Dong Wang , Da Li","doi":"10.1016/j.hcc.2024.100273","DOIUrl":"10.1016/j.hcc.2024.100273","url":null,"abstract":"<div><div>In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services. This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100273"},"PeriodicalIF":3.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review Kubernetes应用程序在异构CPU架构上的性能基准测试：实验回顾

IF 3.2

High-Confidence Computing Pub Date : 2025-03-01 Epub Date: 2024-12-18 DOI: 10.1016/j.hcc.2024.100276

Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman

{"title":"Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review","authors":"Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman","doi":"10.1016/j.hcc.2024.100276","DOIUrl":"10.1016/j.hcc.2024.100276","url":null,"abstract":"<div><div>With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, Kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using Kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering Kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing Kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for Kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100276"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data distribution inference attack in federated learning via reinforcement learning support 通过强化学习支持联合学习中的数据分布推理攻击

IF 3.2

High-Confidence Computing Pub Date : 2025-03-01 Epub Date: 2024-05-17 DOI: 10.1016/j.hcc.2024.100235

Dongxiao Yu , Hengming Zhang , Yan Huang , Zhenzhen Xie

{"title":"Data distribution inference attack in federated learning via reinforcement learning support","authors":"Dongxiao Yu , Hengming Zhang , Yan Huang , Zhenzhen Xie","doi":"10.1016/j.hcc.2024.100235","DOIUrl":"10.1016/j.hcc.2024.100235","url":null,"abstract":"<div><div>Federated Learning (FL) is currently a widely used collaborative learning framework, and the distinguished feature of FL is that the clients involved in training do not need to share raw data, but only transfer the model parameters to share knowledge, and finally get a global model with improved performance. However, recent studies have found that sharing model parameters may still lead to privacy leakage. From the shared model parameters, local training data can be reconstructed and thus lead to a threat to individual privacy and security. We observed that most of the current attacks are aimed at client-specific data reconstruction, while limited attention is paid to the information leakage of the global model. In our work, we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model. Different from other FL attacks that aim to infer individual clients’ raw data, the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model. We argue that such information is valuable since the training data behind a well-trained global model indicates the common knowledge of a specific task, such as social networks and e-commerce applications. To implement such an attack, our key idea is to adopt a deep reinforcement learning approach to guide the attack process, where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution. By a carefully designed Markov decision proces (MDP) process, our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100235"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141036150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modular neural network for edge-based detection of early-stage IoT botnet 基于边缘检测早期物联网僵尸网络的模块化神经网络

IF 3.2

High-Confidence Computing Pub Date : 2025-03-01 Epub Date: 2024-04-16 DOI: 10.1016/j.hcc.2024.100230

Duaa Alqattan , Varun Ojha , Fawzy Habib , Ayman Noor , Graham Morgan , Rajiv Ranjan

{"title":"Modular neural network for edge-based detection of early-stage IoT botnet","authors":"Duaa Alqattan , Varun Ojha , Fawzy Habib , Ayman Noor , Graham Morgan , Rajiv Ranjan","doi":"10.1016/j.hcc.2024.100230","DOIUrl":"10.1016/j.hcc.2024.100230","url":null,"abstract":"<div><div>The Internet of Things (IoT) has led to rapid growth in smart cities. However, IoT botnet-based attacks against smart city systems are becoming more prevalent. Detection methods for IoT botnet-based attacks have been the subject of extensive research, but the identification of early-stage behaviour of the IoT botnet prior to any attack remains a largely unexplored area that could prevent any attack before it is launched. Few studies have addressed the early stages of IoT botnet detection using monolithic deep learning algorithms that could require more time for training and detection. We, however, propose an edge-based deep learning system for the detection of the early stages of IoT botnets in smart cities. The proposed system, which we call EDIT (<u>E</u>dge-based <u>D</u>etection of early-stage <u>I</u>oT Botne<u>t</u>), aims to detect abnormalities in network communication traffic caused by early-stage IoT botnets based on the modular neural network (MNN) method at multi-access edge computing (MEC) servers. MNN can improve detection accuracy and efficiency by leveraging parallel computing on MEC. According to the findings, EDIT has a lower false-negative rate compared to a monolithic approach and other studies. At the MEC server, EDIT takes as little as 16 ms for the detection of an IoT botnet.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100230"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An insider threat detection method based on improved Test-Time Training model 一种基于改进Test-Time Training模型的内部威胁检测方法

IF 3.2

High-Confidence Computing Pub Date : 2025-03-01 Epub Date: 2025-01-14 DOI: 10.1016/j.hcc.2024.100283

Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang

{"title":"An insider threat detection method based on improved Test-Time Training model","authors":"Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang","doi":"10.1016/j.hcc.2024.100283","DOIUrl":"10.1016/j.hcc.2024.100283","url":null,"abstract":"<div><div>As network and information systems become widely adopted across industries, cybersecurity concerns have grown more prominent. Among these concerns, insider threats are considered particularly covert and destructive. Insider threats refer to malicious insiders exploiting privileged access to networks, systems, and data to intentionally compromise organizational security. Detecting these threats is challenging due to the complexity and variability of user behavior data, combined with the subtle and covert nature of insider actions. Traditional detection methods often fail to capture both long-term dependencies and short-term fluctuations in time-series data, which are crucial for identifying anomalous behaviors. To address these issues, this paper introduces the Test-Time Training (TTT) model for the first time in the field of insider threat detection, and proposes a detection method based on the TTT-ECA-ResNet model. First, the dataset is preprocessed. TTT is applied to extract long-term dependencies in features, effectively capturing dynamic sequence changes. The Residual Network, incorporating the Efficient Channel Attention mechanism, is used to extract local feature patterns, capturing relationships between different positions in time-series data. Finally, a Linear layer is employed for more precise detection of insider threats. The proposed approaches were evaluated using the CMU CERT Insider Threat Dataset, achieving an AUC of 98.75% and an F1-score of 96.81%. The experimental results demonstrate the effectiveness of the proposed methods, outperforming other state-of-the-art approaches.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100283"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud data security with deep maxout assisted data sanitization and restoration process 通过深度最大化辅助数据清理和恢复流程保障云数据安全

IF 3.2

High-Confidence Computing Pub Date : 2025-03-01 Epub Date: 2024-05-22 DOI: 10.1016/j.hcc.2024.100238

Shrikant D. Dhamdhere, M. Sivakkumar, V. Subramanian

{"title":"Cloud data security with deep maxout assisted data sanitization and restoration process","authors":"Shrikant D. Dhamdhere, M. Sivakkumar, V. Subramanian","doi":"10.1016/j.hcc.2024.100238","DOIUrl":"10.1016/j.hcc.2024.100238","url":null,"abstract":"<div><div>The potential of cloud computing, an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers. The fast advancements in cloud computing techniques led to the amazing arrival of cloud services. But data security is a challenging issue for modern civilization. The main issues with cloud computing are cloud security as well as effective cloud distribution over the network. Increasing the privacy of data with encryption methods is the greatest approach, which has highly progressed in recent times. In this aspect, sanitization is also the process of confidentiality of data. The goal of this work is to present a deep learning-assisted data sanitization procedure for data security. The proposed data sanitization process involves the following steps: data preprocessing, optimal key generation, deep learning-assisted key fine-tuning, and Kronecker product. Here, the data preprocessing considers original data as well as the extracted statistical feature. Key generation is the subsequent process, for which, a self-adaptive Namib beetle optimization (SANBO) algorithm is developed in this research. Among the generated keys, appropriate keys are fine-tuned by the improved Deep Maxout classifier. Then, the Kronecker product is done in the sanitization process. Reversing the sanitization procedure will yield the original data during the data restoration phase. The study part notes that the suggested data sanitization technique guarantees cloud data security against malign attacks. Also, the analysis of proposed work in terms of restoration effectiveness and key sensitivity analysis is also done.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100238"},"PeriodicalIF":3.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0