Study And Improvement On K-anonymity Of Privacy Protection

In recent years, with the rapid development of science technology and informationtechnology constant, sharing of resources and mutual benefit is paid close attention toby people more and more. When the kinds of information resources bring benefit in lifeto people, they also bring the risk of data privacy information disclosure to us.Protecting people’s privacy information has become a focus of the public concern. Thisis an important subject in data released treatment researching. In data release process, ifonly to delete or encryption identifier that can determine the identity of the users,privacy protection effect is not good. Attackers can still link these databases with otherreleased database on Quasi-identifiers attributes to re-identify individual’s privateinformation. K-anonymity technical in micro-data release is one of the most importantmethods in privacy protection. However, it is a NP-hard problem for optimalK-anonymity on dataset with multiple attributes. The major research of K-anonymityfocuses on how to release data of anonymity in the reasonable time complexity and atthe same time can obtain higher level by anonymity.This paper comprehensive analyzes the existing K-anonymity of algorithms andsums up the advantages and disadvantages of these methods. To solve these problemsthe major work of this paper are as follows:①This paper proposes a multi-dimensional K-anonymity algorithm based onmapping and divide-and-conquer strategy. The algorithm sets up a new mappingMulti-dimensional to single-dimensional model, and records two of importantinformation: the number of data points that each dimension is mapped tosingle-dimensional set, Pro, and number of multi-dimensional data points that eachsingle-dimensional data point is mapped to, PPA. This algorithm adopted informationdependency to measure information changes, which reduces the loss of informationafter K-anonymity. The algorithm can finish in polynomial time complexity, whichimproves the actual application ability of K-anonymity.②This paper proposes an effective k-anonymity strategy based on incrementallocal update on large dataset. For frequent change of data release process, this strategyuse threshold value to maintain relative stability. The strategy realizes local updatemethod by positioning operation to reduce the time cost. This strategy considers theneighbors set in similar set on incremental data correlation degree of information to improve the quality of the result set anonymity.③In the paper, the variety of comparative experiments are in two ways. Oneexperiment is on the experimental data, and the other is on the real data. Theexperimental results show that the multi-dimensional K-anonymity algorithm based onmapping and divide-and-conquer strategy can get a higher level by anonymity, and thetime performance can be accepted; and the effective k-anonymity strategy based onincremental local update on large dataset is efficient compared to the methods at presentand has a good data safety performance.