An Address Desensitization Method Preserving Distribution Characteristics

Abstract

An address desensitization method that retains distribution characteristics, comprising the following steps: (1) collecting address information to be desensitized, and grouping it into a data set M1 to be desensitized; (2) performing a compliance check on the address information data set to be desensitized , put the unqualified address information into the abnormal data set, and generate the compliance data set M2 to be desensitized; (3) perform the first stage digital masking process on the compliance data set to be desensitized, and generate the data to be shuffled Set M3; (4) generate a uniform random sequence L1 according to the linear feedback shift register; (5) remove the number greater than the length M3 of the data set to be desensitized in the uniform random sequence generated in (4), to generate a shuffled sequence L2; (6) Shuffle the address data in M3 with the number in sequence L2 as the position index. The desensitization result of the invention well retains the distribution characteristics of the address data and has strong analyzability; the desensitization result can retain good consistency; the desensitization time is short and has high efficiency.

Description

Technical field [0001] The present invention relates to the field of data processing, and more particularly to a process desensitization method for reserving distribution features. Background technique [0002] In the era of big data, information is unquestionable "wealth." Regardless of the government unit or business, or every person, every day is interested in unintentional collection, storage, sharing data, and larger scale. This information is an invisible asset. But it is precisely because we have more and more data, our leaks are naturally getting higher and higher. In recent years, my country has strengthened the management and research of user data information protection. National convened a number of conferences, big data and data security that have been multi-data, data security, have become popular topics for banks and Internet companies. Guo Jiakai's "Data Deferribly: Sensitive Data Security Guard" proposed the important role of data desensitization in information se...

AI Technical Summary

Problems solved by technology

Therefore, while desensitizing the address information, the analysis value of the address information should be preserved as much as possible. Methods a and b do effectively desensitize the address information, but the retained analysis value is also very limited. The granularity of address distribution If it is too large, it is difficult to dig deeper. If you adjust the masking or generalization granularity of desensitization, for example, only masking to the "road" level, that is, "rockery road, Gongshu District, Hangzhou City, Zhejiang Province**", although the analysis value It is well preserved, but it is also easy for people to "guess" the individual in reality by contacting other information, so the degree of desensitization is too low and the security is low

Method c can achieve desensitization effect by randomly shuffling addresses, but because of its randomness, the desensitization results of the same data source are different every time, which does not conform to the consistency of the desensitization process, and random The result of shuffling may not completely retain the distribution characteristics of the original address information

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