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The volume of data generated from human social interactions is breathtaking, data stemming from human sensors and wearables is on the rise, and soon, machines are poised to start interacting too. This data can be utilized to perform data analytics, which might expose valuable vivid patterns and knowledge that would be imperative to society. In this thesis, we present novel data mining predictive analytics solutions, ranging from location prediction through activity recognition to prediction in the medical domain. On the other hand, such data also contains huge volume of sensitive information that is raising serious privacy concerns. To mitigate such concerns, we present new enhanced privacy preserving data mining techniques to protect data.