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Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. We give a formal security proof for our scheme. Based on the theoretical proof, we can strictly guarantee the privacy of both input dataset and output classification results, i.e., the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we not only theoretically analyze our computation complexity and communication overheads, but also evaluate our implementation cost by leveraging extensive experiments over real dataset, which shows our scheme can achieve practical efficiency.

Providing a creditable basis for access control decision-making is not an easy task for the resource pooling, dynamic, and multi-tenant cloud environment. The trust notation can provide this creditable basis, based on multiple factors that can accurately compute the user’s trust for the granting access entity. In this paper, the formal trust model has been introduced, which presents a novel method to provide the basis for granting access. It is based on three factors and their semantic relations, which investigate important measures for the cloud environment. Also, a new Trust-Based Access Control (TB-AC) model has been proposed. The proposed model supports dynamically changing the user’s assigned permissions based on its trust level. In addition, TB-AC ensures secure resource sharing among potential untrusted tenants. TB-AC has been deployed on a separated VM in our private cloud environment, which is built using OpenStack. The experimental results indicated that TB-AC can evaluate access requests within reasonable and acceptable processing times, which is based on the final trust level calculation and the communication between TB-AC and some of the intended OpenStack services. By considering very rough conditions and huge traffic overhead, the final trust level can be calculated in an average time of 200ms. Furthermore, the communication overhead between TB-AC and each of Keystone, Nova, and Neutron is very light. Finally, TB-AC has been tested under different scenarios and is provable, usable and scalable.

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.

Organizations have come to realize that storing their databases in the Cloud rather than in-house data centers is cheaper and more flexible. However, companies are still concerned about the privacy and the security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the entire dataset, which is impractical.

This paper proposes a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. Data objects are encrypted and sorted using Z-order space-filling curve. An index is built on top of the encrypted data to be utilized by the Service Provider to identify and retrieve a superset of data objects that contains the answers to the query. Many simulation experiments have been carried out to measure the performance of the proposed scheme in terms of the redundancy in data retrieved. The experimental results show that the proposed scheme outperforms the most recent scheme by Kim et al. in terms of data redundancy.

To tackle the problems of encrypted cloud storage in a limited bandwidth environment, we proposed a scheme that divided encryption block of plaintext according to the content changes. The scheme based on the optimized AES algorithm and exhibits improved efficiency and security of data synchronizing in the limited bandwidth environment.

With the development of cloud computing, cloud data security has become the key issue of cloud computing. According to the characteristic of cloud computing has a massive node, based on the theory of human immunity, apply the idea of human immune into cloud computing, propose a cloud security immune system(CSIS) oriented for anomaly detection, and analyze its characteristics. Analysis showed that CSIS can better solve the problems of data security faced within cloud computing.