  Location privacy-preserving mechanisms (LPPMs) have been extensively studied
for protecting users' location privacy by releasing a perturbed location to
third parties such as location-based service providers. However, when a user's
perturbed locations are released continuously, existing LPPMs may not protect
the sensitive information about the user's spatiotemporal activities, such as
"visited hospital in the last week" or "regularly commuting between Address 1
and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and
office), which we call it \textit{spatiotemporal event}. In this paper, we
first formally define {spatiotemporal event} as Boolean expressions between
location and time predicates, and then we define $ \epsilon
$-\textit{spatiotemporal event privacy} by extending the notion of differential
privacy. Second, to understand how much spatiotemporal event privacy that
existing LPPMs can provide, we design computationally efficient algorithms to
quantify the privacy leakage of state-of-the-art LPPMs when an adversary has
prior knowledge of the user's initial probability over possible locations. It
turns out that the existing LPPMs cannot adequately protect spatiotemporal
event privacy. Third, we propose a framework, PriSTE, to transform an existing
LPPM into one protecting spatiotemporal event privacy against adversaries with
\textit{any} prior knowledge. Our experiments on real-life and synthetic data
verified that the proposed method is effective and efficient.
