Technique for preserving privacy on friend recommendation system by using Naive bayes classifier in OSN

Recently, communication and sharing the information through online social network (OSN) grows very fast in day to day life. As user share important data through online social network, therefore providing security and privacy to the online social network becomes the challenging task for the researchers. Moreover, for increasing the social connections and get the information from the particular group of people user needs to search the new friends and add them in the friend list. Now a days there isa friend recommendation method which was used by many online social networks, this method recommended the friend list to the user. As the privacy of these friend recommendation methods is the most significance task for the researchers and for the online social networks. Basically an online social network focused on the millions people's profile to spend time on an online social network, also implementing an OSN clients social circles, by using recommended suggestions. In this system, the main focus is on privacy and security of the online social network. This system basically focuses to support the users of online social network by secure trust creation with a stranger. This is achieved with multi-hop recommendation process. In the proposed method, the system provides privacy to the online social network from which system is allowing users to enhance their social networks. Existing system used secure KNN algorithm for securing social network. By using KNN algorithm, distance based learning is unclear. It is sort of distance and their component to use for the best results and calculation is very expensive. To overcome this limitation and to improve the precision, the proposed frame-work makes use of secure social coordinate matching with a Naive Bayes classifier. Also the privcy of social coordinates is maintained with Pailier encryption algorithm. Experimental results demonstrate that the proposed system is secure and time efficient than the existing one. Memory consumption is also improved with the proposed system.