Privacy calculation method and application of big data application open platform

Abstract

The invention discloses a privacy calculation method and application of a big data application open platform, and the method comprises the following steps: obtaining software information of the big data application open platform needing privacy calculation, building privacy level parameters, calculating the privacy level parameters, and sorting the privacy level parameters according to privacy levels; and according to the arrangement sequence, performing simulation operation on each item data in the software in sequence. The invention comprises performing simulation activity on big data needing to be subjected to privacy calculation by applying open platform software so as to calculate the occurrence probability of privacy data in the simulation activity; determining a privacy degree requirement index of software putaway according to the property and effect of the software; comparing the occurrence probability of the privacy data in the software simulation activity with the privacy degree requirement index of software shelving, so that whether the software can be shelved on the big data application open platform or not is calculated, the privacy protection degree of the software on the big data application open platform is guaranteed, and actual application is facilitated.

Description

technical field [0001] The invention belongs to the field of privacy computing, and specifically relates to a privacy computing method and application of an open platform for big data applications. Background technique [0002] In existing life, with the development of the mobile Internet, smart terminal devices have become the main communication tool for users. The use of many financial technologies depends on the large amount of data generated on smart terminal devices. Traditional big data applications rely on cloud computing, namely Collect data on the mobile terminal, upload all the data to the centralized cloud server, and then use big data, machine learning, artificial intelligence and other technologies to calculate, process and utilize these data, and with the application of big data on the mobile terminal The open platform pays more and more attention to personal privacy and data security, and the data obtained by the open platform of big data applications on mobil...

AI Technical Summary

Problems solved by technology

[0002] In existing life, with the development of the mobile Internet, smart terminal devices have become the main communication tool for users. The use of many financial technologies depends on the large amount of data generated on smart terminal devices. Traditional big data applications rely on cloud computing, namely Collect data on the mobile terminal, upload all the data to the centralized cloud server, and then use big data, machine learning, artificial intelligence and other technologies to calculate, process and utilize these data, and with the application of big data on the mobile terminal The open platform pays more and more attention to personal privacy and data security, and the data obtained by the open platform of big data applications on mobile terminals is mostly personal privacy. There are technical or management factors in the transmission and storage of these data that may lead to information leakage, and eventually It may lead to illegal use of privacy. Regardless of the user's personal privacy protection awareness or relevant information protection policies, it is required to avoid third-party collection, transmission and storage of these private data

[0003] However, the prior art lacks a privacy calculation method based on the big data application open platform, which leads to the inability of the mobile terminal device to calculate the privacy requirements of the software on the big data application open platform to determine whether it meets the requirements. Standards are listed on the shelves, which in turn leads to uneven privacy protection of software on the open platform for big data applications, which is not conducive to practical applications

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