图 书 馆
防止串通的隐私保护数据挖掘Collusion-Resistant Privacy-Preserving Data Mining |
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| 课程网址: | http://videolectures.net/kdd2010_yang_crpp/ |
| 主讲教师: | Bin Yang |
| 开课单位: | 东京大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased concern regarding the protection of private and personal information. Lately, numerous methods of privacy-preserving data mining have been proposed. Most of these methods are based on an assumption that semi-honest is and collusion is not present. In other words, every party follows such protocol properly with the exception that it keeps a record of all its intermediate computations without sharing the record with others. In this paper, we focus our attention on the problem of collusions, in which some parties may collude and share their record to deduce the private information of other parties. In particular, we consider a general problem in PPDM - multiparty secure computation of some functions of secure summations of data spreading around multiple parties. To solve such a problem, we propose a new method that entails a high level of security - full-privacy. With this method, no sensitive information of a party will be revealed even when all other parties collude. In addition, this method is efficient with a running time of O(m). We will also show that by applying this general method, a large number of problems in PPDM can be solved with enhanced security. |
| 关 键 词: | 数据挖掘; 隐私保护; 敏感信息 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：cwx |
| 阅读次数: | 61 |