Privacy measurement algorithm for structured data in data release environment

Abstract

The invention belongs to the technical field of information security, and provides a privacy measurement algorithm for structured data in a data release environment. Firstly, a data source collects user information and generates a user information abstract, an external data set is screened according to abstract information, and attribute similarity is calculated to distinguish private attributes and non-private attributes. Then, external and internal average information amount and distribution information amount of the private attributes are calculated, sensitive attributes for the private attributes are priced and identified based on demand and time loss, quasi-identifiers are extracted by applying a maximum entropy principle, and risk elements are identified in the data. Finally, the data privacy is measured according to the privacy model, a privacy-oriented anonymous technology is given, and it can be proved that the privacy and availability under the orientation reach the best balance.

Description

technical field [0001] The invention relates to a structured data-oriented privacy measurement algorithm in a data publishing environment, and belongs to the technical field of information security. Background technique [0002] The rapid development of big data-related technologies has promoted the importance of data in all walks of life. Various organizations can use large amounts of data to optimize and upgrade their main business through big data technology in order to gain a stronger competitive advantage in the market. At the same time, in order to prevent a few Internet giants who have a deeper grasp of big data technology from using their own advantages in big data technology to monopolize and affect market stability; the voice of data sharing continues to rise. However, the privacy issue accompanying data sharing has become an alternative bottleneck affecting data sharing. If the privacy issue cannot be properly resolved, large-scale data sharing will be difficult t...

AI Technical Summary

Problems solved by technology

However, the existing anonymity technology lacks privacy orientation, that is, it does not protect the data according to the characteristics of the data before anonymizing the original data, but adopts a global and unified operation, which makes some data that already have certain Data with a certain degree of privacy loses this part of privacy and also loses the availability of this part of data, which not only causes a waste of computing overhead, but also makes it one step away from the goal of safe and efficient data sharing.

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