In this most revolutionized world, the social network plays a vital role in each and everyone’s life. Social networking is a pervasive communication platform where the users can search whole over the world via the Internet. Users have similar interest to connect and interact with one another and to share their private and personal interest. In this paper, we examine privacy concern for the social networking users by distributed clustering method. In the proposed scheme, to speed-up, the Kernel k-means algorithm, a prototype based hybrid kernel k-means algorithm is involved in distributing the users into the cluster. Since we are using a large data set, we use a hybrid approach to speed-up the kernel k-means clustering ( HSKK ). The clustering process used here is to partition a similar set of objects in a dataset. Additionally, in the clustering process, a cryptographic protocol such as homomorphic encryption is involved in every dataset to achieve the goal to protect the private data. To prove the efficiency of the proposed approach, the experiment is done on Movie lens dataset. The experimental study of HSKK shows that the proposed method can significantly reduce the computation time and the private data of users is hidden from the service provider.

中文翻译：

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加速隐私保护核K-means聚类的混合方法及其在社会分布式环境中的应用

在这个最具革命性的世界中，社交网络在每个人的生活中都扮演着至关重要的角色。社交网络是一个无处不在的交流平台，用户可以通过互联网在全球范围内进行搜索。用户对彼此联系和互动以及分享他们的私人和个人兴趣有着相似的兴趣。在本文中，我们通过分布式聚类方法检查社交网络用户的隐私问题。在所提出的方案中，为了加速，内核k-means算法，一种基于原型的混合内核k-means算法被用于将用户分配到集群中。由于我们使用的是大型数据集，因此我们使用混合方法来加速内核 k 均值聚类 (HSKK)。这里使用的聚类过程是在数据集中划分一组相似的对象。此外，在聚类过程中，每个数据集都涉及同态加密等密码协议，以达到保护私有数据的目的。为了证明所提出方法的有效性，在电影镜头数据集上进行了实验。HSKK 的实验研究表明，所提出的方法可以显着减少计算时间，并且用户的私人数据对服务提供商是隐藏的。