Ensuring Privacy-Aware Data Release: An Analysis of Applicability of Privacy Enhancing Techniques to Real-world Datasets

The internet and media platforms are store and share user-generated data as a means to provide an intelligent and personalized experience to the user. This user data is the new currency which is powering commercial interests through profiling of user behavior. But as users are becoming increasingly aware of implications due to data breaches, there has been a greater need for privacy preservation methods. The need is further enforced due to legal and commercial implications caused by the privacy compromises. We have analyzed the existing privacy ensuring techniques and their applicability to real-world data. It has been established that while privacy preservation techniques are dependent on the data attributes, the scheme of data release also plays an important role. We have therefore explored the applicability of syntactic and semantic privacy preservation techniques and shown their effects on a dataset.