  The COVID-19 pandemic has prompted technological measures to control the
spread of the disease. Private contact tracing (PCT) is one of the promising
techniques for the purpose. However, the recently proposed Bluetooth-based PCT
has several limitations in terms of functionality and flexibility. The existing
systems are only able to detect direct contact (i.e., human-human contact), but
cannot detect indirect contact (i.e., human-object, such as the disease
transmission through surface). Moreover, the rule of risky contact cannot be
flexibly changed with the environmental situation and the nature of the virus.
In this paper, we propose a secure and efficient trajectory-based PCT system
using trusted hardware. We formalize trajectory-based PCT as a generalization
of the well-studied Private Set Intersection (PSI), which is mostly based on
cryptographic primitives and thus insufficient. We solve the problem by
leveraging trusted hardware such as Intel SGX and designing a novel algorithm
to achieve a secure, efficient and flexible PCT system. Our experiments on
real-world data show that the proposed system can achieve high performance and
scalability. Specifically, our system (one single machine with Intel SGX) can
process thousands of queries on 100 million records of trajectory data in a few
seconds.
