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


Cloud Computing and Big Data have quickly become the most popular forms of computing and data resources because of their ability to fulfill the functional requirements of users. By conducting analysis, conversion, extraction, and refinement on large amounts of data, it is possible to avert the spread of a disease and forecast the actions of a group. However, the private data of each individual user is also a component of Big Data. In order for users to satisfy the functional requirements of the service providers, users are required to provide private data. Some SaaS service providers have not been authorized to collect and analyze the sensitive private data of the user; as a result, the user's private data has been disclosed. This is done so that the SaaS providers can gain economic benefits. In this article, we present a method for the discovery of private data chain disclosures, with the goal of preventing the inappropriate disclosure of sensitive personal information pertaining to users. First, we determine the degree of similarity between the two sets of data and the cost of disclosing the private information. Second, during the process of interaction between the user and the SaaS service, the disclosure chain and key private data are identified based on the degree of similarity and the cost of disclosure. Third, we demonstrate the viability of our discovery framework for the private data chain through a series of experiments, which also serve to demonstrate its effectiveness.

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