Public Cloud Servers Research Articles

Accessing Secured Data in Cloud Computing Environment

Number of businesses using cloud computing has increased dramatically over the last few years due to the attractive features such as scalability, flexibility, fast start-up and low costs. Services provided over the web are ranging from using provider’s software and hardware to managing security and other issues. Some of the biggest challenges at this point are providing privacy and data security to subscribers of public cloud servers. An efficient encryption technique presented in this paper can be used for secure access to and storage of data on public cloud server, moving and searching encrypted data through communication channels while protecting data confidentiality. This method ensures data protection against both external and internal intruders. Data can be decrypted only with the provided by the data owner key, while public cloud server is unable to read encrypted data or queries. Answering a query does not depend on it size and done in a constant time. Data access is managed by the data owner. The proposed schema allows unauthorized modifications detection.

Cooperative Virtual Machine Management in Smart Grid Environment

We focus on the problems of cooperative virtual machine management of cloud users in a smart grid environment. In such an environment, the cloud users can cooperate to share the available computing resources in private cloud and public cloud to reduce the total cost. To achieve an optimal and fair solution, we develop the framework composed of the virtual machine allocation, cost management, and cooperation formation models. The problem is challenging due to the uncertainties (e.g., uncertain power price and unpredictable users' demand). Therefore, for the virtual machine allocation, we develop the stochastic programming model to obtain the optimal solutions of virtual machines to be hosted in the local data center, to be hosted on the public cloud servers, or to be migrated to the data centers of other cooperative cloud users. Then, among cooperative cloud users, the cost management is formulated as the coalitional game whose fair share of the total cost is obtained as the Shapley value. Next, given that the cloud users are rational, we formulate the cooperation formation as the network formation game to analyze the stability of the cooperation. In the experiment, we evaluate our proposed framework with real trace data. The results clearly show that the cooperative virtual machine management can achieve the minimum total cost of cloud users compared with expected value and worst case formulations.

Dynamic Multi-keyword Top-k Ranked Search over Encrypted Cloud Data

Nowadays, more and more people are motivated to outsource their local data to public cloud servers for great convenience and reduced costs in data management. But in consideration of privacy issues, sensitive data should be encrypted before outsourcing, which obsoletes traditional data utilization like keyword-based document retrieval. In this paper, we present a secure and efficient multi-keyword ranked search scheme over encrypted data, which additionally supports dynamic update operations like deletion and insertion of documents. Specifically, we construct an index tree based on vector space model to provide multi-keyword search, which meanwhile supports flexible update operations. Besides, cosine similarity measure is utilized to support accurate ranking for search result. To improve search efficiency, we further propose a search algorithm based on “Greedy Depth-first Traverse Strategy”. Moreover, to protect the search privacy, we propose a secure scheme to meet various privacy requirements in the known ciphertext threat model. Experiments on the real-word dataset show the effectiveness and efficiency of proposed scheme.

Proxy Provable Data Possession in Public Clouds

Recently, cloud computing rapidly expands as an alternative to conventional computing due to it can provide a flexible, dynamic and resilient infrastructure for both academic and business environments. In public cloud environment, the client moves its data to public cloud server (PCS) and cannot control its remote data. Thus, information security is an important problem in public cloud storage, such as data confidentiality, integrity, and availability. In some cases, the client has no ability to check its remote data possession, such as the client is in prison because of committing crime, on the ocean-going vessel, in the battlefield because of the war, and so on. It has to delegate the remote data possession checking task to some proxy. In this paper, we study proxy provable data possession (PPDP). In public clouds, PPDP is a matter of crucial importance when the client cannot perform the remote data possession checking. We study the PPDP system model, the security model, and the design method. Based on the bilinear pairing technique, we design an efficient PPDP protocol. Through security analysis and performance analysis, our protocol is provable secure and efficient.

Efficient multi-keyword ranked query over encrypted data in cloud computing

Cloud computing infrastructure is a promising new technology and greatly accelerates the development of large scale data storage, processing and distribution. However, security and privacy become major concerns when data owners outsource their private data onto public cloud servers that are not within their trusted management domains. To avoid information leakage, sensitive data have to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword-based queries and rank the matching results on the encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result which contains a large number of documents, the out-of-order ranking problem may occur. This makes it hard for the data consumer to find the subset that is most likely satisfying its requirements. In this paper, we propose a flexible multi-keyword query scheme, called MKQE to address the aforementioned drawbacks. MKQE greatly reduces the maintenance overhead during the keyword dictionary expansion. It takes keyword weights and user access history into consideration when generating the query result. Therefore, the documents that have higher access frequencies and that match closer to the users’ access history get higher rankings in the matching result set. Our experiments show that MKQE presents superior performance over the current solutions.