  Time series data have numerous applications in big data analytics. However,
they often cause privacy issues when collected from individuals. To address
this problem, most existing works perturb the values in the time series while
retaining their temporal order, which may lead to significant distortion of the
values. Recently, we propose TLDP model that perturbs temporal perturbation to
ensure privacy guarantee while retaining original values. It has shown great
promise to achieve significantly higher utility than value perturbation
mechanisms in many time series analysis. However, its practicability is still
undermined by two factors, namely, utility cost of extra missing or empty
values, and inflexibility of privacy budget settings. To address them, in this
paper we propose {\it switch} as a new two-way operation for temporal
perturbation, as opposed to the one-way {\it dispatch} operation. The former
inherently eliminates the cost of missing, empty or repeated values. Optimizing
switch operation in a {\it stateful} manner, we then propose $StaSwitch$
mechanism for time series release under TLDP. Through both analytical and
empirical studies, we show that $StaSwitch$ has significantly higher utility
for the published time series than any state-of-the-art temporal- or
value-perturbation mechanism, while allowing any combination of privacy budget
settings.
