Survey of Data Confidentiality and Privacy in the Cloud Computing Environment

The objective of this research is to develop a scheme for improving cloud data confidentiality. A considerable number of people are sharing data through third-party applications in the cloud computing environment. According to reviewed literature, it has been realized that data security and privacy were the key challenges to the wider adoption of cloud services with insider threats being the most prevalent. Similarly, our online survey indicated that 53.3% of the respondents citing insider breaches as the main threat to their organizational data. The survey also confirmed that data security and privacy is one of the greatest barriers to the adoption of cloud services in their organization. Noting the flaws of Attribute-Based Encryption (ABE) and Identity-based encryption (IBE), and with the growth of computing power, applications are constantly being developed which makes them vulnerable to attacks. Since data confidentiality is essential in the provision of information security in the cloud, this paper suggested the development and the deployment of a hybrid attribute-based re-encryption scheme, which is a scheme that bridges the ABE and IBE, to secure data in the cloud computing environment. Keywords: Encryption, Cloud Computing, Data, confidentiality, Privacy DOI: 10.7176/CEIS/11-5-03 Publication date:September 30th 2020

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. However, those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute- Based Encryption (ABE) have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address the user and data privacy problem in a cloud. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access, finegrained privilege control, and more importantly, tolerance to up to (N -2) authority compromise. Our security and performance analysis show that AnonyControl is both secure and efficient for cloud computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201