PIVE provides a Bayesian differentially private location perturbation mechanism which transforms the user’s exact location to a perturbed location in a geo-indistinguishable way while being resilient against Bayesian attacks before reporting it to the servers. The collected location information may open doors to potential misuse and abuse of private location information, exposing users’ travel patterns and uncovering their health state and political views. With the popularity of location based services for navigation, point-of-interest recommendation and social network etc, the companies that offer such services can continuously collect users’ locations. The first contribution of this dissertation research is the development of PIVE, a two-phase Bayesian differential location privacy framework that aims to protect users’ location privacy in location based services while ensuring the service quality. This dissertation research contributes original ideas and innovative techniques in applying differential privacy, a rigorous mathematical framework that offers provable privacy guarantee, to protect data privacy with improving the trade-off between privacy and utility in the era of big data from three perspectives respectively: data collection, data usage, and data publication. Therefore, it is imperative to develop principled privacy preserving approaches to harvesting the power of those big data. At the same time, however, these datasets often encode privacy-sensitive information related to individuals, which raises serious privacy concerns to the society. The availability of these huge amounts of datasets has been driving the breakthrough in deep learning and explosion of data-driven applications for enriching human with life-enhancing experiences. The widespread use of internet-connected mobile devices, internet of things(IoT) and cloud computing has enabled a large scale collection of personal data, including user profiles, daily activities, locations, photos and health states, etc, of millions and billions of users from a wide range of scenarios such as the usage of mobile apps, smart home, and cloud storage services.
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