Interactive Range Queries under Differential Privacy

Differential privacy approaches employ a curator to control data sharing with analysts without compromising individual privacy. The curator’s role is to guard the data and determine what is appropriate for release using the parameter epsilon to adjust the accuracy of the released data. A low epsilon value provides more privacy, while a higher epsilon value is associated with higher accuracy. Counting queries, which ”count” the number of items in a dataset that meet specific conditions, impose additional restrictions on privacy protection. In particular, if the resulting counts are low, the data released is more specific and can lead to privacy loss. This work addresses privacy challenges in single-attribute counting-range queries by proposing a Workload Partitioning Mechanism (WPM) which generates estimated answers based on query sensitivity. The mechanism is then extended to handle multiple-attribute range queries by preventing interrelated attributes from revealing private information about individuals. Further, the mechanism is paired with access control to improve system privacy and security, thus illustrating its practicality. The work also extends the WPM to reduce the error to be polylogarithmic in the sensitivity degree of the issued queries. This thesis describes the research questions addressed by WPM to date, and discusses future plans to expand the current research tasks toward developing a more efficient mechanism for range queries

Privacy-Preserving Trajectory Data Publishing via Differential Privacy

Over the past decade, the collection of data by individuals, businesses and government agencies has increased tremendously. Due to the widespread of mobile computing and the advances in location-acquisition techniques, an immense amount of data concerning the mobility of moving objects have been generated. The movement data of an object (e.g. individual) might include specific information about the locations it visited, the time those locations were visited, or both. While it is beneficial to share data for the purpose of mining and analysis, data sharing might risk the privacy of the individuals involved in the data. Privacy-Preserving Data Publishing (PPDP) provides techniques that utilize several privacy models for the purpose of publishing useful information while preserving data privacy. The objective of this thesis is to answer the following question: How can a data owner publish trajectory data while simultaneously safeguarding the privacy of the data and maintaining its usefulness? We propose an algorithm for anonymizing and publishing trajectory data that ensures the output is differentially private while maintaining high utility and scalability. Our solution comprises a twofold approach. First, we generalize trajectories by generalizing and then partitioning the timestamps at each location in a differentially private manner. Next, we add noise to the real count of the generalized trajectories according to the given privacy budget to enforce differential privacy. As a result, our approach achieves an overall epsilon-differential privacy on the output trajectory data. We perform experimental evaluation on real-life data, and demonstrate that our proposed approach can effectively answer count and range queries, as well as mining frequent sequential patterns. We also show that our algorithm is efficient w.r.t. privacy budget and number of partitions, and also scalable with increasing data size