论文标题
基于差异方法的隐私保护模型，用于云环境中的敏感数据
A Privacy-Preserving Model based on Differential Approach for Sensitive Data in Cloud Environment
论文作者
论文摘要
需要在云环境中与各方和利益相关者共享大量数据和应用程序，以进行存储，计算和数据利用。由于第三方运行云平台，业主无法完全信任此环境。但是，在不同各方之间有效共享数据时，确保保留隐私已成为一个挑战。本文提出了一个新颖的模型，该模型将数据分配到敏感和非敏感的部分，将噪声注入敏感数据中，并使用K-匿名化，差异隐私和机器学习方法执行分类任务。它允许多个所有者出于各种目的在云环境中共享其数据。该模型指定了涉及多个不信任方的通信协议来处理所有者数据。提出的模型通过提供强大的机制来保留实际数据。实验是在心脏病，心律失常，肝炎，印度肝病患者和弗雷明汉数据集上进行的，用于支持向量机，K-北端邻居，随机森林，天真的贝叶斯和人工神经网络分类器，以根据精确，精确，回忆和F1分数计算效率。与以前的工作相比，所达到的结果可提供高准确性，精度，召回和F1得分高达93.75％，94.11％，100％和87.99％的评分，并分别提高了16％，29％，12％和11％的进步。
A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.
