Private Data Exploration with Provable Guarantees

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Computing technology has enabled massive digital traces of our personal lives to be collected and stored. Numerous research projects and real-life applications crucially rely on the exploration of these datasets, but there are great privacy concerns as demonstrated by the recent passing of the General Data Protection Regulation (GDPR) in Europe and the controversial Facebook privacy scandal. A simple ad-hoc privacy-preserving approach can lead to a drastic cost to individuals and companies. Existing database systems with strong privacy guarantee such as differential privacy provide no direct, optimal guarantees for what the analysts really care, including the error in the query answer (accuracy) and the waiting time before receiving the answer (latency). With insufficient privacy background, the analysts can easily suffer poor accuracy or long waiting time when using these systems. Naively applying existing data exploration techniques, such as sampling or online aggregation, to the private setting can either break the privacy guarantee or result in poor accuracy. Moreover, these systems offer the same privacy guarantee and cannot meet the diverse privacy requirements of different applications. My proposed research aims primarily at enabling private data exploration with provable guarantees such that all parties involved are not required to be privacy experts -- data analysts can naturally explore sensitive data with a rich set of query templates and functionalities while data curator can easily track the privacy loss over the entire exploration process. This research program will build on my current work on customized privacy and private exploration systems. To do so, my research program includes a set of short-term and long-term research tasks. The short-term research plan aims to (1) develop accuracy-aware data explorations under the state-of-the-art privacy guarantee, differential privacy, and (2) enable private sampling and online aggregation to enhance the interactive user experience during data exploration. The long-term research plan is to support customizable privacy guarantees other than differential privacy in private data exploration. The proposed frameworks will be tested through prototyping with open-sourced database systems.