With the proliferation of mobile devices and various sensors (e.g., GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users' privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e., the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93% F-score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.