Data Privacy Management, Cryptocurrencies and Blockchain Technology - ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings
Data Privacy Management, Cryptocurrencies and Blockchain Technology - ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings
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数据隐私管理、加密货币和区块链技术 - ESORICS 2018 国际研讨会、DPM 2018 和 CBT 2018，西班牙巴塞罗那，2018 年 9 月 6-7 日，会议记录
DOI：
10.1007/978-3-030-00305-0_17
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wagner I
中科院分区：
文献类型：
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作者：
Wagner I
隐私风险评估旨在分析和量化与新系统相关的隐私风险。因此，它们对于确保充分的隐私保护至关重要。然而，目前量化隐私风险的方法在很大程度上依赖于经验丰富的分析师在例如五点量表上选择“正确”的风险水平。在本文中，我们认为，一个更科学的量化隐私风险增加的准确性和可靠性，从而可以更容易地建立隐私友好的系统。我们讨论了如何隐私侵犯的影响和可能性可以分解和量化，并强调有意义的指标和测量单位的重要性。我们提出了一种量化和表示隐私风险的方法，该方法考虑了一系列因素以及各种上下文和攻击者模型。最后，我们确定了一些主要的研究问题，进一步采取这种方法在各种应用场景。
Privacy risk assessments aim to analyze and quantify the privacy risks associated with new systems. As such, they are critically important in ensuring that adequate privacy protections are built in. However, current methods to quantify privacy risk rely heavily on experienced analysts picking the “correct” risk level on e.g. a five-point scale. In this paper, we argue that a more scientific quantification of privacy risk increases accuracy and reliability and can thus make it easier to build privacy-friendly systems. We discuss how the impact and likelihood of privacy violations can be decomposed and quantified, and stress the importance of meaningful metrics and units of measurement. We suggest a method of quantifying and representing privacy risk that considers a collection of factors as well as a variety of contexts and attacker models. We conclude by identifying some of the major research questions to take this approach further in a variety of application scenarios.