Localized differential privacy city junk data report and privacy calculation method

Abstract

The invention discloses a localized differential privacy city junk data report and privacy calculation method, which comprises the following specific steps of: setting a position privacy parameter anda junk amount level privacy parameter, and calculating a privacy processing parameter; obtaining the original real data of the user by positioning the position of the junk point and judging the junkamount level number of the junk point; respectively carrying out localized differential privacy processing on the position data and the garbage amount level data and uploading the data to a server; the server receiving the data uploaded by the user and carrying out statistics and denoising processing on the data; and the server carrying out aggregation calculation on the de-noised data to obtain the real garbage quantity level of each position and form an urban garbage distribution map. On the premise of protecting the positions and data privacy of all data collectors, the urban garbage dynamic distribution target based on mobile group perception can be achieved.

Description

technical field [0001] The invention relates to the field of information security and privacy protection, in particular to a localized differential privacy city garbage data report and privacy calculation method. Background technique [0002] With the continuous improvement of sensor accuracy and the popularization of mobile smart devices, mobile devices represented by smart phones can perceive people and surrounding environmental information anytime and anywhere, so that people can obtain the status of their environment in a timely manner, and better Provide the required services for people and society, and promote the continuous development of the field of mobile group perception. Mobile group sensing provides people with a new way of perceiving the world, enabling anyone to perform sensing tasks through mobile terminals to participate in the sensing process and provide people with various services. [0003] In the application of mobile groups, whether the privacy of user...

AI Technical Summary

Problems solved by technology

Traditional encryption methods have disadvantages such as high computational overhead and various privacy protection methods based on anonymization cannot resist background knowledge attacks. The centralized differential privacy method needs to be based on the honesty and credibility of third-party data collectors, but will not steal or leak users. The assumption of sensitive information is not realistic in practice

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