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Privacy-Preserving Design of Data Processing Systems in the Public Transport Context

机译:公共交通环境下数据处理系统的隐私保护设计

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The public transport network of a region inhabited by more than 4 million people is run by a complex interplay of public and private actors. Large amounts of data are generated by travellers, buying and using various forms of tickets and passes. Analysing the data is of paramount importance for the governance and sustainability of the system. This manuscript reports the early results of the privacy analysis which is being undertaken as part of the analysis of the clearing process in the Emilia-Romagna region, in Italy, which will compute the compensations for tickets bought from one operator and used with another. In the manuscript it is shown by means of examples that the clearing data may be used to violate various privacy aspects regarding users, as well as (technically equivalent) trade secrets regarding operators. The ensuing discussion has a twofold goal. First, it shows that after researching possible existing solutions, both by reviewing the literature on general privacy-preserving techniques, and by analysing similar scenarios that are being discussed in various cities across the world, the former are found exhibiting structural effectiveness deficiencies, while the latter are found of limited applicability, typically involving less demanding requirements. Second, it traces a research path towards a more effective approach to privacy-preserving data management in the specific context of public transport, both by refinement of current sanitization techniques and by application of the privacy by design approach.
机译:一个人口超过400万人居住的地区的公共交通网络是由公共和私人行为者之间复杂的相互作用来运作的。旅行者,购买和使用各种形式的门票和通行证会产生大量数据。分析数据对于系统的治理和可持续性至关重要。该手稿报告了隐私分析的早期结果,该分析是意大利艾米利亚-罗马涅地区清算流程分析的一部分,该过程将计算从一个运营商处购买并与另一运营商一起使用的机票的补偿。在手稿中通过示例显示清除数据可用于违反有关用户的各种隐私方面以及有关运营商的(技术上等效的)商业秘密。随后的讨论有两个目标。首先,它表明,在研究了可能的现有解决方案之后,既可以回顾有关一般隐私保护技术的文献,又可以通过分析世界各地不同城市正在讨论的类似情况,发现前者存在结构有效性缺陷,而后者后者被发现适用性有限,通常要求不高。其次,它通过改进当前的消毒技术和通过设计方法应用隐私来追踪一条研究路径,以寻求一种在公共交通的特定环境下更有效的隐私保护数据管理方法。

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