1Big Data Institute, Central South University, Changsha 410083, China 2National Engineering Laboratory for Medical Big Data Application Technology,Central South University, Changsha 410083, China 3Life Science College, Central South University, Changsha 410083, China
[Objective] This paper tries to improve the efficiency of k-anonymity algorithm and the quality of published data. [Methods] Based on the recognition rates and multi-branch-tree forest structure, we designed a new k-anonymous algorithm (MFBRR). It conducted bottom-up reviews of data according to properties of the generalization tree, and calculated the recognition rates. Then, we selected the target leaf nodes to prune the tree, which reduced the information loss. Finally, the MFBRR-γ algorithm was proposed based on parallel computing and multi-thread processing. [Results] We evaluated our algorithms with hierarchical precision and operation time using the “Adult” data sets. The hierarchical precisions of MFBRR and MFBRR-γ were 0.97 and 0.88. It took the MFBRR and MFBRR-γ algorithms 1 457 minutes and 12.08 minutes (γ=100) to process 30,000 data sets. The MFBRR algorithm achieved hierarchical precision of 0.93 with health care data. [Limitations] We only examined our models with two data sets. [Conclusions] The proposed algorithms could reduce the information loss due to anonymity and improve the quality of published data.
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