Disclosure-Compliant Query Answering

In today's data-driven world, organizations face increasing pressure to comply with data disclosure policies, which require data masking measures and robust access control mechanisms. This paper presents Mascara, a middleware for specifying and enforcing data disclosure policies. Mascara extends traditional access control mechanisms with data masking to support partial disclosure of sensitive data. We introduce data masks to specify disclosure policies flexibly and intuitively and propose a query modification approach to rewrite user queries into disclosure-compliant ones. We present a utility estimation framework to estimate the information loss of masked data based on relative entropy, which Mascara leverages to select the disclosure-compliant query that minimizes information loss. Our experimental evaluation shows that Mascara effectively chooses the best disclosure-compliant query with a success rate exceeding 90%, ensuring users get data with the lowest possible information loss. Additionally, Mascara's overhead compared to normal execution without data protection is negligible, staying lower than 300ms even for extreme scenarios with hundreds of possible disclosure-compliant queries.