|
|||||
|
|
| 带学习的同步隐私保护频繁模式挖掘 | |
| 引用本文： | 郭宇红,童云海,唐世渭,吴冷冬.带学习的同步隐私保护频繁模式挖掘[J].软件学报,2011,22(8):1749-1760. |
| 作者姓名： | 郭宇红 童云海 唐世渭 吴冷冬 |
| 作者单位： | 1. 国际关系学院信息科技系,北京100091;北京大学机器感知与智能教育部重点实验室,北京 100871 2. 北京大学机器感知与智能教育部重点实验室,北京,100871 3. Department of Computing Science, University of Alberta, Edmonton T6G 2R3, Canada |
| 基金项目： | 国家自然科学基金(60403041,60473072) |
| 摘 要： | 为了提高挖掘结果的准确性,提出基于样例学习和项集同步随机化的隐私保护频繁模式挖掘方法(learning and synchronized privacy preserving frequent pattern mining,简称LS-PPFM).该方法充分利用不需要隐私保护的个体数据,首先对不需要保护的数据学习,得到样... |
| 关 键 词： | 有指导的 基于学习的 随机化 隐私保护 频繁模式挖掘 |
| 收稿时间： | 2010/4/16 0:00:00 |
| 修稿时间： | 2011/1/20 0:00:00 |
Learning and Synchronized Privacy Preserving Frequent Pattern Mining |
|
| GUO Yu-Hong,TONG Yun-Hai,TANG Shi-Wei and WU Leng-Dong.Learning and Synchronized Privacy Preserving Frequent Pattern Mining[J].Journal of Software,2011,22(8):1749-1760. | |
| Authors: | GUO Yu-Hong TONG Yun-Hai TANG Shi-Wei and WU Leng-Dong |
| Affiliation: | GUO Yu-Hong1,2,TONG Yun-Hai2,TANG Shi-Wei2,WU Leng-Dong3 1(Department of Information Technology,University of International Relations,Beijing 100091,China) 2(Key Laboratory of Machine Perception(Ministry of Education),Peking University,Beijing 100871,China) 3(Department of Computing Science,University of Alberta,Edmonton T6G 2R3,Canada) |
| Abstract: | To improve the accuracy of mining results,this paper proposes a method of privacy preserving frequent pattern mining,based on sample learning and synchronized randomization of itemset(LS-PPFM).The method utilizes the data of individuals who do not care privacy.First,the data that does not need protecting are learned,and some strongly associated items are obtained.Then,when the data is randomized,the associated items are bound together and randomized synchronously to try to keep their potential associations.... |
| Keywords: | supervised learning-based randomization privacy preserving frequent pattern mining |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
| 点击此处可从《软件学报》浏览原始摘要信息 | |
| 点击此处可从《软件学报》下载全文 | |