Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

Date

2021

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

ACM

Type

Article

Peer reviewed

Yes

Abstract

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined, and how. In this paper, we tackle the first problem, i.e. the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e. higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Citation

Wagner, I. and Yevseyeva, I. (2020) Designing Strong Privacy Metrics Suites Using Evolutionary Optimization. ACM Transactions on Privacy and Security,

Rights

Research Institute

Cyber Technology Institute (CTI)