Widespread web application adoption created a large, complex and varied amount of data known as Big Data. These data sets have a great value for many economic and scientific sectors, however they come with additional difficulties when it comes to storing and analyzing them. Big Data Analytics is the term that describes the process of researching this massive amount of information in order to find hidden patterns and correlations. Business Intelligence departments can now support decision-making processes based on this broad range of data points collected throughout the lifetime of an application and the designated user’s interaction with it. However, the abundance and extensive use of Big Data comes with a number of security and privacy risks that must be addressed. This work identifies and analyzes these concerns as well as their requirements. Focusing on user privacy, some of the major issues include: over collection of data in mobile applications, misuse of data, and multi-source data analysis. These issues can not always be solved using existing privacy preserving methods. The variety and velocity of Big Data makes it difficult to distinguish between sensitive and nonsensitive information, so traditional anonymization techniques can not always be used. Furthermore, analyzing multi-source datasets can lead to risks of user reidentification. In this paper we investigate proposed solutions for securing Big Data as well as ways to maintain data privacy. We look into two major use cases: healthcare and web analytics, where Big Data is becoming more and more important. We sum up with a comparison of the requirements and solutions used to preserve user data privacy for the statistical and clinical data collected in today’s applications.