Contact tracing is a critical tool in containing epidemics such as COVID-19. Researchers have carried out a lot of work on contact tracing. However, almost all of the existing works assume that their clients and authorities have large storage space and powerful computation capability and clients can implement contact tracing on their own mobile devices such as mobile phones, tablet computers, and wearable computers. With the widespread outbreaks of the epidemics, these approaches are of less robustness to a larger scale of datasets when it comes to resource-constrained clients. To address this limitation, we propose a publicly verifiable contact tracing algorithm in cloud computing (PvCT), which utilizes cloud services to provide storage and computation capability in contact tracing. To guarantee the integrity and accuracy of contact tracing results, PvCT applies a novel set accumulator-based authentication data structure whose computation is outsourced, and the client can check whether returned results are valid. Furthermore, we provide rigorous security proof of our algorithm based on the -Strong Bilinear Diffie–Hellman assumption. Detailed experimental evaluation is also conducted on three real-world datasets. The results show that our algorithm is feasible within milliseconds of client CPU time and can significantly reduce the storage overhead from the size of datasets to a constant 128 bytes.

中文翻译：

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PvCT：云计算中可公开验证的联系人跟踪算法

接触者追踪是遏制 COVID-19 等流行病的关键工具。研究人员在接触者追踪方面进行了大量工作。然而，几乎所有现有的工作都假设其客户和当局拥有较大的存储空间和强大的计算能力，并且客户可以在自己的移动设备（如手机、平板电脑和可穿戴电脑）上实现接触者追踪。随着流行病的广泛爆发，当涉及到资源受限的客户端时，这些方法对更大规模的数据集的稳健性较差。为了解决这个限制，我们提出了一种云计算中可公开验证的联系人跟踪算法（PvCT），它利用云服务在联系人跟踪中提供存储和计算能力。为了保证接触者追踪结果的完整性和准确性，PvCT采用了一种新颖的基于集合累加器的认证数据结构，其计算外包，客户端可以检查返回的结果是否有效。此外，我们为我们的算法提供了严格的安全证明，基于-强双线性 Diffie-Hellman 假设。还对三个真实世界的数据集进行了详细的实验评估。结果表明，我们的算法在客户端 CPU 时间的毫秒内是可行的，并且可以将存储开销从数据集的大小显着降低到恒定的 128 字节。