Efficient twoparty privacypreserving collaborative kmeans clustering protocol supporting both storage and computation outsourcing Abstract   Nowadays, cloud computing has developed well and been applied in many kinds of areas. However, privacy is still the most challenging problem which obstructs it being applied in some privacysensitive fields, such as finance and government. Advanced cryptographic algorithms provide data privacy with encryption, which can also support computation on such encrypted data. However, new challenge arises when such ciphertexts come from different parties. In particular, how to execute collaboratively data mining on encrypted data coming from different parties is a key issue from cloud service point of view. This paper focuses on privacy problem on outsourced kmeans clustering scheme for two parties. In particular, each partyxe2x80x99s data are encrypted only once and then stored in cloud. The proposed privacypreserving kmeans collaborative clustering protocol is executed mainly at the cloud, with     O  (  k  (  m    n  )  )     rounds of interactions among the two parties and the cloud, where m and n represent the total numbers of records for the two parties, respectively. It is shown that the protocol is secure in the semihonest security model and in the malicious model in which only one party is corrupted during the process of centroids recomputation. Both theoretical and experimental analysis of the proposed scheme are also provided.

Hybrid Approach to SpeedUp the Privacy Preserving Kernel Kmeans Clustering and its Application in Social Distributed Environment In this most revolutionized world, the social network plays a vital role in each and everyonexe2x80x99s 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 speedup, the Kernel kmeans algorithm, a prototype based hybrid kernel kmeans algorithm is involved in distributing the users into the cluster. Since we are using a large data set, we use a hybrid approach to speedup the kernel kmeans 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.