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 text during the computing process, it is difficult to protect the private data in interactive and shared computing using traditional information security technology.

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