4.7 Article

Ontology-Based Privacy Data Chain Disclosure Discovery Method for Big Data

Journal

IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 15, Issue 1, Pages 59-68

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2019.2921583

Keywords

Ontology; privacy disclosure detection; privacy data chain; similarity metric

Funding

  1. National Natural Science Foundation of China [61602262]
  2. Jiangsu Natural Science Foundation of China [BK20150865, BK20130735]
  3. China Postdoctoral Science Foundation [2016M591842]
  4. Jiangsu province Postdoctoral Science Foundation [1601198C]
  5. Nature Science Foundation of Jiangsu for Distinguished Young Scientist [BK20170039]

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In this paper, a method for private data chain disclosure discovery is proposed to prevent the illegal disclosure of a user's sensitive privacy information.
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 users 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 cisclosed. 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.

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