A Privacy-Preserving High-Order Neuro-Fuzzy c-means Algorithm with Cloud Computing

In the real world massive heterogeneous details and data are generated from the cloud. Heterogeneous data is basically a different type of data combined together. Processing on the heterogeneous data is done with the help of neuro-fuzzy technology. This becomes a hot topic for cloud. We propose a privacy-preserving high-order neuro-fuzzy c-means algorithm for clustering heterogeneous data on the cloud. Privacy-preserving high-order neuro-fuzzy c-means algorithm on cloud computing clusters the heterogeneous data set by representing each heterogeneous data object as a tensor and uses the tensor distance to capture the correlations in the high-order tensor space. Furthermore, the cloud computing is employed to improve the clustering efficiency for massive heterogeneous data from cloud. The BGV encryption mechanism or technique is used to protect the private data when performing the high-order neuro-fuzzy c-means algorithm on to the cloud. We propose a practical privacy-preserving c-means clustering scheme that can be efficiently outsourced to cloud servers. Our scheme allows cloud servers to perform clustering directly over encrypted datasets, while achieving com-parable computational complexity and accuracy compared with clustering s over unencrypted ones. We also find out the secure integration of MapReduce into our mechanism, which makes our mechanism or we can say scheme mostly suitable for cloud computing environment. By security survey and numerical survey carry out the performance of our mechanism in terms of security and efficiency.