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.

中文翻译：

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使用进化优化设计强大的隐私度量套件

准确、一致地衡量隐私的能力是开发新的隐私保护的关键。然而，最近的研究发现了现有隐私指标的弱点，以及仅使用单一隐私指标导致的弱点。如果我们能够解决两个未解决的问题：应该组合哪些指标以及如何组合，指标套件或隐私指标的组合是一种很有前途的机制来缓解这些弱点。在本文中，我们解决了第一个问题，即为强度量套件选择度量，方法是将其表述为具有单目标和多目标的背包优化问题。由于需要为每个指标套件评估大量组合和许多质量/目标，因此很难准确地解决这个问题，我们应用了 16 种现有的进化和元启发式优化算法。我们解决了三个隐私应用领域的优化问题：基因组隐私、图隐私和车辆通信隐私。我们发现生成的度量套件比以前提出的度量套件具有更好的属性，即更高的单调性、多样性、均匀性和共享值范围。