Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework

Abstract

Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engaging in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provides privacy-conscious organizations with a way to monitor the usage of the application data made available to third parties in terms of privacy. The proposed approach, without loss of generality, is applicable to BigSQL-style technologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of the privacy score.

Digital Privacy Under Attack: Challenges and Enablers

“…Query AuditingModifying Query Processing Adding Noise (Output noise injection) [59], [83], [35], [76] Differential Attack…”

“…If given the privacy definition and the answers to past queries, any new query that leads to private information disclosure should be denied. A quantitative privacy metric called PriDe is proposed by Khan et al [59], serving as a tool to calculate the privacy risk score when querying a private database, which can be deployed in an interactive query environment to monitor and protect data privacy. As a popular customizable technology and analysis platform, SAIL DATABANK also uses the idea of a privacy governance model to provide anonymous data for reference in various fields after rigorous auditing disclosing risks [54].…”