Every day, we use digital devices like smartphones, tablet computers, etc., for most of our routine work, leaving lots of data generated from every activity. We use social media for our social needs, resulting in the sharing of private data. With most of the apps on our mobile phones, we share our private data knowingly or unknowingly. All the corporations collecting our data will use it for analysis to find patterns that can be used for better customer relationship management. In today’s knowledge economy, every corporation uses its huge accumulated data for business growth using decision support systems. However, most data are unstructured and semi-structured and contains customers’ private data. Data analysis is mostly done by a third party to get hidden patterns and insights for their strategic information. Preserving the privacy of individuals is vital for corporations before analyzing it for pattern mining. In today’s big data scenario, the application of privacy preserving techniques is very challenging in terms of scalability and complexity because of the huge amount of data and variety of data as most of the data are unstructured. As big data analytics is indispensable for every corporation to get an edge over their competitors, at the same time preserving the privacy of individuals is equally important. This paper analyzes the recent advances in Privacy Preserving Data Mining and analytics in big data in terms of efficiency and complexity of implementation of privacy preservation techniques and the decrease in data utility due to the effects of privacy preservation. Privacy preserving big data analytics is a less explored research area by researchers. This paper gives the roadmap for future research with better anonymization techniques for data mining and analytics in big data.