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Privacy preservation of online user behavior data is important aspect in data mining, as of late, the study of privacy preserving data mining or networking has been studied mainly due to the extensive proliferation of confidential data on the internet. As online social networking sites attract thousands of million people to use it every day for different purpose, due to this lots of data is being generated every day on internet. Online user behavior analysis and security is becoming increasingly important, as it offers valuable information which can be useful for research purpose or analyst to develop better e-commerce strategies. However, it raises significant privacy concern of users data. Data aggregation scheme is generally used for privacy concern. In this paper, for privacy preservation of multiple online users behavior data, we have studied and analyzed multiple techniques. The main objective behind this survey paper is to percepting the existing privacy preservation and analysis technique of user behavior data and to find a better techniques and algorithms as a way to offer excessive efficiency and scalability. Subsequently, several methods for the privacy preservation and analysis have been suggested to attain the highest relevance. |
