3 research outputs found
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
The k-means clustering is one of the most popular clustering algorithms in
data mining. Recently a lot of research has been concentrated on the algorithm
when the dataset is divided into multiple parties or when the dataset is too
large to be handled by the data owner. In the latter case, usually some servers
are hired to perform the task of clustering. The dataset is divided by the data
owner among the servers who together perform the k-means and return the cluster
labels to the owner. The major challenge in this method is to prevent the
servers from gaining substantial information about the actual data of the
owner. Several algorithms have been designed in the past that provide
cryptographic solutions to perform privacy preserving k-means. We provide a new
method to perform k-means over a large set using multiple servers. Our
technique avoids heavy cryptographic computations and instead we use a simple
randomization technique to preserve the privacy of the data. The k-means
computed has exactly the same efficiency and accuracy as the k-means computed
over the original dataset without any randomization. We argue that our
algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems
Security. Springer, Cham, 201
Secure Algorithm for File Sharing Using Clustering Technique of K-Means Clustering
In the current scenario The Security is most or of at most importance when we are talking about file transferring in networks. In the thesis, the work has design a new innovative algorithm to securely transfer the data over network. The k –means clustering algorithm, introduced by MacQueen in 1967 is a broadly utilized plan to solve the clustering problem. It classifies a given arrangement of n-information focuses in m-dimensional space into k-clusters whose focuses are gotten by the centroids. The issue with the privacy consideration has been examined, and that is the data is distributed among various gatherings and the disseminated information is to be safeguarded. In this thesis, created chucks or parts of file using the K-Means Clustering Algorithm and the individual part is encrypted using the key which is shared between sender and receiver. Further, the bunched records have been encoded by utilizing AES encryption algorithm with the introduction of private key concept covertly shared between the involved parties which gives a superior security state