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Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation 利用任意和扰动增强数据挖掘中隐私的混合方法
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.029074
B. Murugeshwari, S. Rajalakshmi, K. Sudharson
Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.
想象一下,有很多客户,每个客户都有个人数据;个人输入严重损坏,服务器只关心这些数据的集体的、统计上重要的方面。在一些数据挖掘方法中,隐私已经变得非常重要。因此,各种保护隐私的数据分析技术应运而生。因此,我们使用随机化过程来准确地重建复合数据属性。此外,我们使用隐私措施来估计需要多少欺骗来保证隐私。有几种可行的隐私保护措施;然而,确定哪一个是最好的仍然是一个正在进行的工作。本文讨论了测量隐私的难度,同时也提供了大量随机抽样程序和统计和分类数据结果。此外,本文还研究了任意扰动性质在隐私保护中的应用。根据这项研究,任意物体(尤其是随机矩阵)都可以“预测”频率模式。它展示了如何使用任意晶格光谱选择策略从被随机数损坏的样本中恢复关键信息。该过滤系统的概念框架假设,广泛的实践发现表明,稀疏数据扭曲在各种情况下保持相对适度的隐私保护。因此,研究框架在维护数据隐私和安全方面是高效和有效的。
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引用次数: 4
Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks 雾-车联网中的高效成本任务卸载方案
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032316
Alla Abbas Khadir, S. Hosseini-Seno, B. F. Dhahir, R. Budiarto
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引用次数: 1
Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN 基于改进混合群智能的无线传感器网络能量优化
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036106
A. N. Ahmed, Jinhyung Kim, Yun-Seung Nam, M. Abouhawwash
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引用次数: 0
Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet 基于无批处理归一化深度卷积神经网络的拥挤场景鲁棒计数
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037706
S. Zahir, R. Khan, M. Ullah, Muhammad Ishaq, Naqqash Dilshad, Amin Ullah, Mi Young Lee
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引用次数: 1
Graph-Based Replication and Two Factor Authentication in Cloud Computing 云计算中的基于图的复制和双因素认证
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.029040
S. Lavanya, N. Saravanakumar
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引用次数: 0
Biometric Verification System Using Hyperparameter Tuned Deep Learning Model 基于超参数调优深度学习模型的生物特征验证系统
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.034849
Mohammad Yamin, Saleh Bajaba, Sarah B. Basahel, E. Lydia
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引用次数: 0
Improved QoS-Secure Routing in MANET Using Real-Time Regional ME Feature Approximation 基于实时区域ME特征逼近的MANET qos安全路由改进
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036916
Y. John, G. Ravi
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引用次数: 0
A General Linguistic Steganalysis Framework Using Multi-Task Learning 使用多任务学习的通用语言隐写分析框架
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037067
Lingyun Xiang, Rong Wang, Yuhang Liu, Yangfan Liu, Lina Tan
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引用次数: 0
Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors 基于智能手机传感器步态模式识别的人格评估
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036185
Kainat Ibrar, Abdul Muiz Fayyaz, M. A. Khan, Majed Alhaisoni, U. Tariq, Seob Jeon, Yun-Seong Nam
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引用次数: 0
Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture 基于迁移学习和定位感知CNN架构的大规模鱼类图像分类和定位
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.031008
Usman Ahmad, Muhammad Junaid Ali, Faizan Ahmed Khan, Arfat Ahmad Khan, A. ur Rehman, Malik Muhammad Ali Shahid, Mohd anul haq, Ilyas Khan, Zamil S. Alzamil, Ahmed Alhussen
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引用次数: 2
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