Requirement-Oriented Privacy Protection Analysis Architecture in Cloud Computing

Abstract

As a new software paradigm, cloud computing provides services dynamically according to user requirements. However, it is difficult to control personal privacy information because of the opening, virtualization, multi-tenancy and service outsourcing characters. Therefore, how to protect user privacy information has become a research focus. In this paper, we propose requirement-oriented privacy protection theory analysis architecture and implementation platform. Firstly, the theory analysis architecture is depicted as layers, and we analyze the function and key technologies of every layer. Secondly, we address the privacy property description method with ontology and description logic, and then analyze the theory modules of privacy items conflict checking layer, privacy policy negotiation layer and privacy agreement forensics layer. Thirdly, according to theory architecture, we design the implementation platform of requirement-oriented privacy protection, and discuss the function and workflow. In the end, we conclude and point out the future work. Index Terms-Privacy protection, privacy items, privacy policy, privacy property, cloud computing I. INTRODUCTION Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [1]. There are some characters, such as service outsourcing, virtualization, distribution and multi-tenancy. These characters enhance the service quality and save the computing resources, for example, service outsourcing enhances the service capability and specialization through service composition [2]. However, the transparency of privacy information to the outsourcing service provider, make users worry that privacy data be illegally propagated and used.

Application of Personal Information Privacy Protection Based on Machine Learning Algorithm

“…This scheme ensures that statistical queries on the target dataset and on datasets that differ by only one record are identical in results [ 15 ]. Ke et al [ 16 ] proposed the use of differential privacy computing to achieve privacy-preserving mining of joint datasets [ 16 ]. Barni et al [ 17 ] proposed a distributed algorithm, which assumes that the same record has a unique global identifier in a vertically divided data environment, and all parties involved in data fusion only have data with partial attributes; the original information is hidden in the communication process, and the privacy protection of the data fusion process is realized by constructing a complete anonymity table to determine whether the anonymity threshold is met [ 17 ].…”

The Energy-Consumption of CPU and Memory Prediction in Cloud Computing Based on GREY-ARIMA Model

“…Some scholars research and propose a kind of forecasting method of cloud computing load resources matched with the mode in this field, such as Caron, et al [2] , and this method used improving the string matching algorithms (KMP). Saripalli, et al [5] proposed a method to identify the resources load peak and forecasting.. Prevost, et al [6] use the neural network and self-regression model to forecast the loads of different types of resources.…”

“…However, due to the randomness task of cloud computing platform, sparse and dense degree of different machine work is different, which causes the existence of spare resources wasting [1]. The prediction of resource consumption's status becomes a vital factors to improve the efficiency of cloud computing platform [2], [3]. Therefore, the prediction of data center in cloud computing has a significant value to research.…”