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.
翻译:需要与云层环境中的各方和利益攸关方共享大量数据和应用程序,用于存储、计算和数据利用。由于第三方操作云平台,所有者无法完全信任这一环境。然而,在各方之间有效分享数据时,确保隐私保护是一项挑战。本文件提出了一个新模式,将数据分成敏感和不敏感部分,将噪音输入敏感数据,并使用k-匿名、差异隐私和机器学习方法执行分类任务。它允许多个所有者为各种目的共享云层环境中的数据。模型指定了多个不受信任的当事方之间的通信协议,用于处理拥有者数据。拟议模型通过提供强健机制保存实际数据。实验针对心脏病、甲状腺炎、Hepatitis、印度-肝脏-住院和Framingham数据集进行,用于支持矢量机器、K-Nearest Neighbor、随机森林、Nive Bayes和人工神经网络分类,以计算准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、准确性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性、性