Visual query method and device for multi-data-source merged data

Abstract

The invention discloses a visual query method and device for multi-data-source merged data, belongs to the field of visual query and privacy protection, and comprises the following steps: 1) a serveris established among N participants; 2) according to the visual request, each participant calculates local data Di, and i belongs to [1, N]; 3) a random number vector is exchanged between every two participants, and the local data Di, the received random number vector and the sent random number vector are fused; meanwhile, a fusion result of the N participants is uploaded to the server; and 4) theserver merges the fusion results uploaded by the N participants and returns the merged results to the front end for visual analysis by the front end, so that the data of the plurality of clients is merged without iteration, an accurate visual query result can be obtained only by one-time processing, and the privacy of the data can be prevented from being leaked.

Description

technical field [0001] The invention relates to the fields of visual query and privacy protection, in particular to a visual query method and device for merging data from multiple data sources. Background technique [0002] In the era of big data, the emphasis on data privacy and security has become a worldwide trend. Therefore, there are great risks in large-scale collection of sensitive data. As the European Union began to implement the General Data Protection Regulation (GDPR) on May 25, 2018, all countries are strengthening the protection of data security and privacy, and paying more attention to users' personal privacy and data security. However, in most industries, data exists in the form of isolated islands. Due to issues such as industry competition, privacy security, and complex administrative procedures, even among different departments of the same company, there are many obstacles to achieving data integration. The cost of integrating data scattered in various pl...

AI Technical Summary

Problems solved by technology

[0004] However, the above patents only realize the exchange of data between internal and external networks, and it is a one-to-one exchange. In the field of visual query, people often need to perform partial visual analysis from the merged data of multiple data sources, and need Relatively Accurate Query Results

Due to the existence of the above-mentioned data island problem, it is a very difficult task to merge different data sources while satisfying the accuracy of the merged results under the premise of ensuring privacy

There are many existing methods such as differential privacy and federated learning. The former adds a large amount of randomization to each data source, resulting in a sharp decline in its usability, especially for some complex queries, sometimes the randomization results will be largely concealed. Real results; the latter requires multiple iterations and repeated training, which is expensive and does not guarantee absolute accuracy

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