Big-data-oriented privacy information release exposure chain discovery method

Abstract

The invention discloses a big-data-oriented privacy information release exposure chain discovery method which is based on the theory of ontology mapping. The method comprises the following steps: (1) measuring and calculating the definition similarity simd, attribute similarity simT and structure similarity sims of privacy data; (2) measuring and calculating the exposure cost of the privacy data through an exposure vector of the privacy data; (3) acquiring the characteristic attribute of a privacy data information exposure chain through measurement and calculation of the definition similarity and exposure cost of the privacy data; and (4) discovering a privacy exposure chain in data released by a user according to the characteristic of the exposure chain, thus protecting the safety in the process of user privacy data information release. Most of the existing privacy protection technologies use a way of encryption or anonymity. However, privacy data must be plaintext when participating in cloud computing. The method is a privacy data release method in the computing process, and can effectively prevent user's privacy data from being leaked in the process of multi-party service computing.

Description

technical field [0001] The invention belongs to the technical field of privacy data security protection for big data users, and relates to a discovery method for big data-oriented privacy exposure chains, in particular to a discovery method for big data-oriented private information publishing exposure chains. Background technique [0002] Big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within an acceptable time frame, and has the characteristics of large quantity, variety, and real-time changes. According to statistics, an average of 2 million users use Google search per second, Facebook users share more than 4 billion information every day, and Twitter handles more than 340 million tweets every day; and the annual data volume is growing exponentially, of which 3 / 4 are contributed by individuals as they create or move digital files, e.g. a typical American office worker contributes 1.8 million MB of data...

AI Technical Summary

Problems solved by technology

Since the traditional information security technology cannot be realized in the multi-party computing process, that is, the user's private data must be in plain te

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