• 1Dr AKash Saxena
• Ajitendra Kumar Singh

Data mining is a promising emerging technology that can facilitate knowledge discovery of large amounts of data. A number of abstraction and implementation scenarios have been demonstrated to deal with relational data issues in a secure manner. As cloud computing grows stronger, more and more data owners are able to outsource data storage and even data processing capabilities. Because user data privacy issues are critical, sensitive data should be encrypted before being stored in the cloud service. In addition, all data mining operations (such as clustering) are performed only on encrypted data. This work demonstrates the comparison between the application of hybrid PSO (particle swarm optimization) fuzzy logic clustering technique and the effective proof KNN method. The cloud data storage and query client is implemented in MATLAB and communicates via the TCP/IP protocol. All data stored on the cloud is encrypted, and all operations on the cloud data only occur on the encrypted data. User query data is also encrypted and the results are encrypted, which provides maximum data privacy and security.

Relational Data Mining, Cloud Data, Cryptography, Privacy Preserving (Cloud Mining), K-NN classifier, Hybrid Fuzzy PSO Classifier.

"Hybrid PSO-Fuzzy Logic Data Clustering Technique for Data Mining on Encrypted Cloud Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.540-547, November-2018, Available :http://www.jetir.org/papers/JETIR1811678.pdf

"Hybrid PSO-Fuzzy Logic Data Clustering Technique for Data Mining on Encrypted Cloud Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp540-547, November-2018, Available at : http://www.jetir.org/papers/JETIR1811678.pdf