Keywords : privacy, privacy preservation, spatio-temporal, behavior science, information discovery
Abstract: Nowadays, we watch an unprecedented growth in data production by humans in almost every aspect of our lives. Along with this, the advantage of more powerful data mining and information discovery techniques have increasingly placed important roles in today’s business and economy.
Nevertheless, the continuum endeavor of extracting information from big data and the results achieved so far have spotted lights on users’ privacy and the disclosure of sensitive private information that are out of control of those who produce data, specially those sensitive information related to human behaviors. Simple behaviors as going to work, walking, running, working out, and having a meeting are being monitored by several sensors presented in smartphones and wearables, and can be inferred from geo-location, temperature, pulse, microphone, and other data generated by these devices. The tendency of these algorithms is to become more powerful by extracting increasingly more accurate information and detailed behaviors.
In this context, the goal of this work is to provide a solution that can serve as a contract between data producer and data consumer that specifies consumer’s data mining techniques to be used to extract information from her/his personal data set while still preserving data producer’s privacy.