Pre-trained Language Models (PLM) have enabled a cost-effective approach to handling various downstream applications via Parameter-Efficient-Fine-Tuning (PEFT) techniques. In this context, service providers have introduced a popular fine-tuning-based product service known as Model-as-a-Service (MaaS). This service offers users access to extensive PLMs and training resources. With MaaS, users can fine-tune, deploy, and utilize their customized models seamlessly, leveraging a one-stop platform that allows them to work with their private datasets efficiently. However, this service paradigm has recently been exposed to the possibility of leaking user private data. To this end, we identify the data privacy leakage risks in MaaS-based PEFT and propose a Split-and-Privatize (SAP) framework, mitigating the privacy leakage by integrating split learning and differential privacy into MaaS PEFT. Furthermore, we propose Contributing-Token-Identification (CTI), a novel method to balance model utility degradation and privacy leakage. As a result, the proposed framework is comprehensively evaluated, demonstrating a 65% improvement in empirical privacy with only a 1% degradation in model performance on the Stanford Sentiment Treebank dataset, outperforming existing state-of-the-art baselines.

Tremor, a prevalent symptom in various neurological disorders, significantly impacts patients' quality of life. Regular and precise tremor monitoring is essential for optimizing treatment effectiveness. Existing at-home monitoring solutions have limitations in daily life due to low adherence, privacy concerns, or inevitable interference from confounding body components and overlapped motions. In this paper, we propose mmTremor, the first privacy-preserving contactless system to achieve practical tremor detection during activities of daily living (ADL) in real-world settings. We design a mmWave-depth fusion tracking algorithm to handle the high interference from confounding body components and a multimodal spatiotemporal deep learning pipeline to effectively exploit latent tremor information. Additionally, a spatial contrastive unsupervised adaptation is proposed for better discriminability and adaptation capability to unseen domains. To fully assess mmTremor in real-world scenarios, we collect a diverse dataset of 28 patients and 9 healthy subject simulations in over 20 distinct environments, including offices, hospitals, and homes. Extensive evaluations demonstrate that mmTremor achieves a high macro-F1 of 0.877 in tremor detection, showcasing the potential as a transformative contactless solution for tremor monitoring in daily life. The dataset will be open-source to facilitate future research.