Abstract- Privacy preserving issues in big data have emerged globally. One of the challenging and emerging issues is the social big data from social networks and social media. It has become increasingly popular because it allows the sharing of individual’s private information for analysis purposes. Facebook, twitter, linkedIn etc. have created huge amount of data at an unprecedented rate with the collaborations of social networks. Traditional data analytics may not be able to handle such large quantities of data and so, maintaining its privacy is a major concern. In this paper, we present various privacy preserving issues and gave a concise and a systematic review of existing techniques of privacy preserving social network. k-anonymity provides protection against identity disclosure but it does not provide sufficient protection against attribute disclosure. This paper initially discusses anonymization and its alternatives for better privacy protection and then it presented a revision of a new technique Differential Privacy which is an alternative approach to anonymization and a big hope for big data to allow for efficient data mining and information fusion from social networks.