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An efficient blind filter: Location privacy protection and the access control in FinTech

机译：高效的盲过滤器：FinTech中的位置隐私保护和访问控制

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摘要

Financial technology(FinTech) is a new item in the financial industry, which has become a popular item that describes novel technologies adopted by the financial service institutions. This term covers a large range of techniques, from data security to financial service. Specially, user privacy protection is generally considered one of the most significant aspects in the financial security domain and preserving data carrying privacy is a critical task in producing a privacy protection strategy, e.g., one of the crucial issues in mobile finance is to ensure the legitimate mobile device users can efficiently search inclusive information from servers without leaking the user privacy. More precisely, more and more mobile finance APP(e.g., AliPay, China Unionpay Quick Pass) has the auxiliary tool or third-party services function that enable users make a location-based services(LBS) query, while the LBS usually carry users' location privacy and that data of service providers should be accessed by legitimate users. In order to address this problem, in this paper, we propose a privacy-preserving LBS framework which supports the query area is a square area based on the user's location, and achieves fine-grained access control on the financial service provider data, user's privacy(especially location privacy), confidentiality of the service provider data, and accurate query result. More precisely, our framework also uses redundant point-of-interesting(POI) records to protect privacy against LBS provider(LBSP), but employs a semi-trusted third party(called proxy) to filter out redundant POI records. We propose a novel blind filter protocol based on comparable attribute-based encryption(CABE) and "transformation" technique, which can filter out the encrypted POI records under the condition that both LBSP and proxy without knowing the user's location information. In comparison with existing solutions, our framework not only realize access control on service provider data innately, but also incurs lower communication and computation overhead on the user side. The analysis and the experiments indicate that our framework is secure and efficient for mobile devices in terms of computation and the communication overhead. (C) 2019 Elsevier B.V. All rights reserved.
机译：金融技术（FinTech）是金融业的一个新兴项目，已成为描述金融服务机构采用的新颖技术的流行项目。该术语涵盖了从数据安全到金融服务的广泛技术。特别地，用户隐私保护通常被认为是金融安全领域中最重要的方面之一，保存携带隐私的数据是制定隐私保护策略的关键任务，例如，移动金融中的关键问题之一就是确保合法性。移动设备用户可以有效地从服务器搜索包含信息，而不会泄露用户隐私。更准确地说，越来越多的移动金融应用程序（例如，支付宝，中国银联快速通行证）具有辅助工具或第三方服务功能，使用户能够进行基于位置的服务（LBS）查询，而LBS通常承载用户的位置隐私以及服务提供商的数据应由合法用户访问。为了解决这个问题，本文提出了一种隐私保护的LBS框架，该框架支持查询区域是基于用户位置的正方形区域，并实现对金融服务提供商数据，用户隐私的细粒度访问控制。 （尤其是位置隐私），服务提供商数据的机密性以及准确的查询结果。更准确地说，我们的框架还使用冗余兴趣点（POI）记录来保护隐私免受LBS提供者（LBSP）的侵害，但使用半信任的第三方（称为代理）来过滤出冗余POI记录。我们提出了一种基于可比的基于属性的加密（CABE）和“转换”技术的新颖盲过滤协议，该协议可以在LBSP和代理都不知道用户的位置信息的情况下过滤出加密的POI记录。与现有解决方案相比，我们的框架不仅在本质上实现了对服务提供商数据的访问控制，而且在用户端降低了通信和计算开销。分析和实验表明，就计算和通信开销而言，我们的框架对于移动设备而言是安全高效的。 （C）2019 Elsevier B.V.保留所有权利。

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