Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

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 article, 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.

Application-Oriented Selection of Privacy Enhancing Technologies

“…Privacy Metric A technology's suitability and effectiveness need to be measurable to evaluate its added privacy gain and monitor the system over time. Note that the broader problem of creating a metric suite that comprehensively covers the notion of privacy is a research problem out of scope for this paper (see for example Wagner and Yevseyeva [43]). A number of privacy metrics are reviewed in [42].…”