AUTHOR=Khan Muhammad Imran , Foley Simon N. , O'Sullivan Barry 

TITLE=Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework

JOURNAL=Frontiers in Big Data

VOLUME=Volume 3 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.00011

DOI=10.3389/fdata.2020.00011

ISSN=2624-909X

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) engag- ing 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 provide privacy conscious organisations with a way to monitor the usage the application data, made available to third-parties, in privacy sense. The proposed ap- proach, without the loss of generality, is applicable to BigSQL style tech- nologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of privacy score.