  Speech is a common input method for mobile embedded devices, but cloud-based
speech recognition systems pose privacy risks. Disentanglement-based encoders,
designed to safeguard user privacy by filtering sensitive information from
speech signals, unfortunately require substantial memory and computational
resources, which limits their use in less powerful devices. To overcome this,
we introduce a novel system, XXX, optimized for such devices. XXX is built on
the insight that speech understanding primarily relies on understanding the
entire utterance's long-term dependencies, while privacy concerns are often
linked to short-term details. Therefore, XXX focuses on selectively masking
these short-term elements, preserving the quality of long-term speech
understanding. The core of XXX is an innovative differential mask generator,
grounded in interpretable learning, which fine-tunes the masking process. We
tested XXX on the STM32H7 microcontroller, assessing its performance in various
potential attack scenarios. The results show that XXX maintains speech
understanding accuracy and privacy at levels comparable to existing encoders,
but with a significant improvement in efficiency, achieving up to 53.3$\times$
faster processing and a 134.1$\times$ smaller memory footprint.
