Organizations who collect sensitive data, such as hospitals or governments, may want to share the data with others. There could be multiple applications or analysts that want to use this data. Directly releasing the data could violate the privacy of individual data contributors. To address this privacy concern, differential privacy [1,2] has arisen as a popular technique for allow for sensitive data analysis. It frequently works through the addition of randomized noise to the output of the analysis, which is controlled through the privacy parameter or budget ε. This noise affects the utility of the analyses, where a smaller budget allocation results in larger noise values, and some applications may set accuracy requirements on the output to restrict the amount of noise added [3,9,10].

Differential privacy has become an appealing choice for analyzing sensitive data while offering strong privacy protection, even for complex data types like graphs. Despite a decade of academic efforts in designing differentially private algorithms for graph analysis, few works have been used in practice. This is due to their complexity in the choice of privacy guarantees and parameter/environmental configurations, or due to their scalability issues for large datasets.

Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) provide bounded and flexible privacy guarantees to the data curators of sensitive data and (ii) support accurate and efficient data exploration by data analysts. However, the specification and processing of customized privacy policies incur additional performance cost, especially for datasets with a large domain. To address this challenge, we propose dynamic Blowfish privacy which allows for the dynamic generation of smaller privacy policies and their data representations at query time. BlowfishDB ensures same levels of accuracy and privacy as one would get working on the static privacy policy. In this demonstration of BlowfishDB, we show how a data curator can fine-tune privacy policies for a sensitive dataset and how a data analyst can retrieve accuracy-bounded query answers efficiently without being a privacy expert.