qiyuangong/1M_Generalization

This repository has been archived on 30/Nov/2023
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MIT License
Created about 9 years ago
Updated almost 8 years ago

qiyuangong/1M_Generalization

qiyuangong

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1M_Generalization is a simple anonymization algorithm for 1:M dataset. It contains two sub-algorithms: Mondrian (for relational part) and Partition (transaction part). Both of them are straight forward, and can be repalced by more powerful algorithm with limtied modification.

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Python & JAVA Solutions for Leetcode

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本文档适合于刚入学的硕士和博士(计算机专业最好，其他专业可参考)。

Mondrian

Python Implementation for Mondrian Multidimensional K-Anonymity (Mondrian).

Basic_Mondrian

The raw mondrian is designed for numerical attributes. When comes to categorical attributes, Mondrian needs to transform categorical attributes to numerical ones. This transformations is not good for some applications. In 2006, LeFevre proposed basic Mondrian, which support both categorical and numerical attributes. This repository is an implementation for basic Mondrian.

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Top_Down_Greedy_Anonymization

Top_Down_Greedy_Anonymization is a Top-down greedy algorithm data anonymization algorithm for relational dataset, proposed by Jian Xu in his papers.

Google_APAC

My solution for Google APAC

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This repository is an python implement of Apriori_based_Anonymization for set-valued dataset anonymization.

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Relational_Transaction_Anon

This repository is an python implement of Relation and Transaction Anonymization for Relational and Transaction dataset anonymization.

Semi_Partition

Semi_Partition is a Top-down greedy algorithm data anonymization algorithm for relational dataset. It's more efficient than Mondrian, which is proposed by Kristen LeFevre in his papers.

PAA

Partition and Anatomize Anonymization

Partition_for_Transaction

This repository is an open source python implement for Partition_for_Transaction. I implement this algorithm in python for further study.

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EC based Anonymization. Improve efficiency without increasing information loss.