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.
Malicious Code Detection for Trusted Execution Environment Based on Paillier Homomorphic Encryption
