Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.

/pdf/the-rise-of-cloud-computing-data-protection-privacy-and-open-12433eik.pdf

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges—A Systematic Literature Review (SLR)

Privacy-preserving data outsourcing in the cloud via semantic data splitting

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.

Privacy Preserved Secure Offloading in the Multi-access Edge Computing Network

Privacy-preserving data splitting is a technique that aims to protect data privacy by storing different fragments of data in different locations. In this work we give a new combinatorial formulation to the data splitting problem. We see the data splitting problem as a purely combinatorial problem, in which we have to split data attributes into different fragments in a way that satisfies certain combinatorial properties derived from processing and privacy constraints. Using this formulation, we develop new combinatorial and algebraic techniques to obtain solutions to the data splitting problem. We present an algebraic method which builds an optimal data splitting solution by using Gr\"{o}bner bases. Since this method is not efficient in general, we also develop a greedy algorithm for finding solutions that are not necessarily minimal sized.

Privacy-preserving Data Splitting: A Combinatorial Approach

With the rapid development of cloud computing and storage technologies, an increasing number of businesses and individuals are using cloud to store or backup data. However, when users upload private data to the cloud, they lose absolute control over the data. With the continued development of cloud storage, the protection of users' privacy has become a pertinent issue to be addressed. Currently, most users make use of a mainstream encryption algorithm to encrypt data before uploading and to decrypt the data after downloading and before use. To solve this problem, in this paper, we propose a new cloud storage privacy protection method that involves bit split (BS) and bit combination (BC). BS is performed on data and reassembled to form numerous part-files before these are uploaded to the different cloud storage servers. After downloading, the part-files from multiple cloud servers are combined to re-create the original file. Experiments show that this method is effective in protecting the privacy of the users' data, with a performance improvement of ~20 times compared with traditional encryption and decryption methods.

http://ieeexplore.ieee.org/iel7/6880679/6885035/06885340.pdf

Data privacy protection using multiple cloud storages

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Data privacy protection using multiple cloud storages