PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment
PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment
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DOI：
10.1145/3490491
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Fumiyuki Kato;Yang Cao;Yoshikawa Masatoshi
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文献类型：
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作者：
Fumiyuki Kato;Yang Cao;Yoshikawa Masatoshi
现有的基于蓝牙的私人接触者追踪（PCT）系统可以私下检测人们是否与COVID-19患者有直接接触。然而，我们发现现有的系统缺乏功能性和灵活性，这可能会影响接触者追踪的成功。具体来说，它们无法检测到间接接触（例如，人们可能通过在餐馆使用被污染的床单而没有与感染者直接接触而暴露于COVID-19）；他们也不能灵活地改变“危险接触”的规则，例如暴露的持续时间或与COVID-19患者的距离（空间和时间）被认为会导致暴露的风险，这可能会随着环境情况而变化。在这篇文章中，我们提出了一个有效和安全的接触追踪系统，使我们能够追踪直接接触和间接接触。为了解决上述问题，我们需要利用用户的轨迹数据进行PCT，我们称之为基于轨迹的PCT，我们将这个问题形式化为一个同时满足安全性和效率要求的时空私有集交集。通过分析不同的方法，例如可以扩展以解决此问题的同态加密，我们将可信执行环境（TEE）确定为实现我们需求的候选方法。主要的挑战是如何在有限的TEE安全内存下设计时空私有集交叉口的算法。为此，我们设计了一个基于tee的系统，采用灵活的轨迹数据编码算法。我们在实际数据上的实验表明，所提出的系统可以在几秒钟内处理数千万条轨迹数据记录的数百条查询。
Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into direct contact with patients with COVID-19. However, we find that the existing systems lack functionality and flexibility, which may hurt the success of contact tracing. Specifically, they cannot detect indirect contact (e.g., people may be exposed to COVID-19 by using a contaminated sheet at a restaurant without making direct contact with the infected individual); they also cannot flexibly change the rules of “risky contact,” such as the duration of exposure or the distance (both spatially and temporally) from a patient with COVID-19 that is considered to result in a risk of exposure, which may vary with the environmental situation. In this article, we propose an efficient and secure contact tracing system that enables us to trace both direct contact and indirect contact. To address the above problems, we need to utilize users’ trajectory data for PCT, which we call trajectory-based PCT. We formalize this problem as a spatiotemporal private set intersection that satisfies both the security and efficiency requirements. By analyzing different approaches such as homomorphic encryption, which could be extended to solve this problem, we identify the trusted execution environment (TEE) as a candidate method to achieve our requirements. The major challenge is how to design algorithms for a spatiotemporal private set intersection under the limited secure memory of the TEE. To this end, we design a TEE-based system with flexible trajectory data encoding algorithms. Our experiments on real-world data show that the proposed system can process hundreds of queries on tens of millions of records of trajectory data within a few seconds.