Assuring privacy when big brother is watching
Assuring privacy when big brother is watching
复制标题
DOI：
10.1145/882082.882102
复制
发表时间：
2003-06
期刊：
影响因子：
--
通讯作者：
Murat Kantarcioglu;Chris Clifton
中科院分区：
文献类型：
--
作者：
Murat Kantarcioglu;Chris Clifton
国土安全措施正在增加收集、处理和挖掘的数据量。与此同时，数据所有者对其隐私和数据潜在滥用提出了合理的担忧。保护隐私的数据挖掘技术可以在不侵犯隐私的情况下实现学习模型。本文解决了一个补充问题：如果我们想应用一个模型而不透露它怎么办？本文提出了一种在不透露数据或规则的情况下应用分类规则的方法。此外，可以验证规则不使用“禁止”标准。
Homeland security measures are increasing the amount of data collected, processed and mined. At the same time, owners of the data raised legitimate concern about their privacy and potential abuses of the data. Privacy-preserving data mining techniques enable learning models without violating privacy. This paper addresses a complementary problem: What if we want to apply a model without revealing it? This paper presents a method to apply classification rules without revealing either the data or the rules. In addition, the rules can be verified not to use "forbidden" criteria.