Differential privacy-based data exception detection method

Abstract

The invention discloses a differential privacy-based data exception detection method. According to the method, the clustering is conducted firstly and then the exception detection is conducted. The method comprises the following steps of adding random noise to the weight of the edge in a minimum spanning tree generated by a prim algorithm by using a noise mechanism in the differential privacy so as to hide the relevance between data objects. Meanwhile, the method uses a judgment criterion of fusing dissimilarity and inverse similarity number to detect abnormality. By adopting the method, the problem that parameters need to be preset and the abnormal data selection is inaccurate in the traditional top-n method can be solved. The method provided by the invention has higher robustness and stronger adaptability, and experimental analysis of the simulation data set and the real data set shows that the method provided by the invention can effectively ensure the security of private data in anenvironment with non-uniform data distribution, improve the recall ratio of anomaly detection and reduce the misjudgment rate.

Description

technical field [0001] The invention relates to data privacy protection and data anomaly detection, in particular to a data anomaly detection method based on differential privacy protection. Background technique [0002] In the context of the rapid development of the Internet, data sharing has become an inevitable link in today's data mining field, and the leakage of personal privacy data in the sharing has aroused people's concerns. In recent years, the leakage of personal privacy information has emerged one after another. In 2016, it was revealed that Uber employees used user information management channels to spy on historical records and track the whereabouts of celebrities and their predecessors. In August 2018, customer data such as personal identity information and private room opening records of 130 million users of Huazhu's hotels were leaked. On the same day, the courier industry giant SF Express was also exposed that more than 300 million pieces of data were susp...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the problem that the data anomaly detection process is vulnerable to attacks from background knowledge, resulting in the leakage of private information, the present invention provides a data anomaly detection method based on differential privacy

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