Digital watermark data tracing method based on attribute importance index

Abstract

The invention discloses a digital watermark data tracing method based on an attribute importance index, and the method specifically comprises the following steps: 1, summarizing original data to be distributed, and extracting a prediction attribute and a class label attribute of each piece of original data to form a data table; 2, creating a watermark index table according to a data receiver of the original data, and generating a key KEY; 3, forming a non-important attribute set attr; 4, embedding a watermark to obtain a data set containing the watermark; 5, distributing the data set containing the watermark according to the information of the data receivers in the watermark index table, and collecting suspected leakage data which is completely or partially leaked in the distribution process or after distribution to form a suspected leakage data set; 6, extracting all sub-watermarks in each piece of data in the suspected leakage data set, and connecting the sub-watermarks with complete watermarks; and 7, extracting the complete watermark, and finding out a corresponding data receiver through the watermark index table, namely an individual with leaked data, so as to complete data leakage traceability.

Description

technical field [0001] The invention relates to the field of data traceability, in particular to a digital watermark data traceability method based on an attribute importance index. Background technique [0002] With the rapid development of data transmission and sharing technology, cross-system data transmission is becoming more and more frequent. These data contain sensitive information of data owners. How to prevent authorized objects from obtaining data from unauthorized forwarding has become an urgent problem to be solved in data security transmission. . For example, data owners such as governments and business organizations have a large amount of data. In order to extract valuable information and knowledge from the data, the data needs to be sent to multiple different third-party data analysis agencies for analysis and processing, which may appear untrustworthy. The third party forwards the received data to others, resulting in illegal forwarding of data and leakage o...

AI Technical Summary

Problems solved by technology

[0004] The existing methods are mainly insufficient in considering both data availability and watermark security, especially focusing on the research of watermark security. Failure to combine the distribution characteristics of data in the process of watermark embedding will cause great damage to data availability; at the same time, its basic The assumption is that the data is complete during the distribution process, but in reality, the data leaker may only leak part of the data tuples, resulting in the destruction of the watermark embedded in the data, which has a great impact on watermark extraction and leaker traceability

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