{"title":"对w-事件差分隐私机制的效用进行基准测试——当基线成为强大的竞争者时","authors":"Christine Schäler, Thomas Hütter, Martin Schäler","doi":"10.14778/3594512.3594515","DOIUrl":null,"url":null,"abstract":"\n The\n w\n -event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of\n w\n -event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent.\n \n \n To this end, we formalize an empirical study of\n w\n -event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.\n","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors\",\"authors\":\"Christine Schäler, Thomas Hütter, Martin Schäler\",\"doi\":\"10.14778/3594512.3594515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The\\n w\\n -event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of\\n w\\n -event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent.\\n \\n \\n To this end, we formalize an empirical study of\\n w\\n -event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.\\n\",\"PeriodicalId\":20467,\"journal\":{\"name\":\"Proc. VLDB Endow.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proc. VLDB Endow.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14778/3594512.3594515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proc. VLDB Endow.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14778/3594512.3594515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
w -event框架是确保连续监控数据流上的差异隐私的当前标准。在w事件差分隐私的基础上,提出了实现该框架的各种机制。它们在实证研究中的可比性对于从业者选择合适的机制和研究人员识别当前的局限性并提出新的机制至关重要。通过进行文献调查,我们观察到现有研究的结果很难比较,部分本质上不一致。为此,我们通过重新出现我们调查中发现的元素,形式化了w事件机制的实证研究。我们介绍了对这些元素的要求,以确保实验结果的可比性。此外,我们提出了一个满足所有要求的基准,并建立了一种评估现有机制和新提议机制的新方法。通过大规模的实证研究,我们对现有机制的优缺点获得了宝贵的新见解。一个意想不到的(但可以解释的)结果是基线至上,也就是说,使用两种基线机制中的一种有望提供良好甚至最佳的效用。最后,我们为从业者提供了选择合适的机制和研究人员改进方案的指南。
Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors
The
w
-event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of
w
-event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent.
To this end, we formalize an empirical study of
w
-event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.