97 research outputs found
Efficient and Private Federated Trajectory Matching
Federated Trajectory Matching (FTM) is gaining increasing importance in big
trajectory data analytics, supporting diverse applications such as public
health, law enforcement, and emergency response. FTM retrieves trajectories
that match with a query trajectory from a large-scale trajectory database,
while safeguarding the privacy of trajectories in both the query and the
database. A naive solution to FTM is to process the query through Secure
Multi-Party Computation (SMC) across the entire database, which is inherently
secure yet inevitably slow due to the massive secure operations. A promising
acceleration strategy is to filter irrelevant trajectories from the database
based on the query, thus reducing the SMC operations. However, a key challenge
is how to publish the query in a way that both preserves privacy and enables
efficient trajectory filtering. In this paper, we design GIST, a novel
framework for efficient Federated Trajectory Matching. GIST is grounded in
Geo-Indistinguishability, a privacy criterion dedicated to locations. It
employs a new privacy mechanism for the query that facilitates efficient
trajectory filtering. We theoretically prove the privacy guarantee of the
mechanism and the accuracy of the filtering strategy of GIST. Extensive
evaluations on five real datasets show that GIST is significantly faster and
incurs up to 3 orders of magnitude lower communication cost than the
state-of-the-arts.Comment: 14 page
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