Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party
Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party
复制标题
DOI：
10.1587/transfun.e92.a.1868
复制
发表时间：
2009-08
期刊：
影响因子：
--
通讯作者：
Chunhua Su;F. Bao;Jianying Zhou;T. Takagi;K. Sakurai
中科院分区：
文献类型：
--
作者：
Chunhua Su;F. Bao;Jianying Zhou;T. Takagi;K. Sakurai
互联网的快速发展为人们提供了大量的数据收集、知识发现和协同计算的机会。但也带来了敏感信息泄露的问题。个人和企业都可能遭受大量数据收集和不信任方的信息检索。在本文中，我们提出了一个隐私保护协议的分布式核密度估计为基础的聚类。我们的方案采用随机数据扰动（RDP）技术和可验证秘密共享来解决[4]中分布式核密度估计的安全性问题，该方案假设了一个中间方来帮助计算。
The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in [4] which assumed a mediate party to help in the computation.