Ontology-Based Privacy Data Chain Disclosure Discovery Method for Big Data
Changbo Ke, Fu Xiao, Zhiqiu Huang, Yunfei Meng, Yan Cao · IEEE Transactions on Services Computing · 2019
To meet user's functional requirements, cloud computing and big data have become the most commonly used computing and data resources. Based on analysis, conversion, extraction and refinement for the big data, a disease can be prevented and group behavior can be predicted. However, each user's private data is also an element in big data. Users must provide private data to the service providers to meet their functional requirements. To gain economic benefits, some SaaS service providers have not been authorized to collect and analyze the user's sensitive private data, as a result, the user's private data is disclosed. In this paper, we propose a private data chain disclosure discovery method, to prevent a user's sensitive privacy information from being illegally disclosed. First, we measure the similarity degree and cost of the disclosure of the private data. Second, according to the similarity degree and cost of disclosure, the disclosure chain and key private data are detected in the process of interaction between user and SaaS service. Third, we propose a discovery framework for the private data chain and demonstrate its feasibility and effectiveness by experiments.