Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework

University of Pittsburgh Pittsburgh, P A, USA User-controllable privacy is important in modern sensing systems, as privacy preferences can vary significantly from person to person and may evolve over time. This is especially relevant in devices equipped with Inertial Measurement Unit (IMU) sensors, such as smartphones and wearables, which continuously collect rich time-series data that can inadvertently expose sensitive user behaviors. While prior work has proposed privacy-preserving methods for sensor data, most rely on static, predefined privacy labels or require large quantities of private training data, limiting their adaptability and user agency. In this work, we introduce PrivCLIP, a dynamic, user-controllable, few-shot privacy-preserving sensing framework. PrivCLIP allows users to specify and modify their privacy preferences by categorizing activities as sensitive (blacklisted), non-sensitive (white-listed), or neutral (gray-listed). Leveraging a multimodal contrastive learning approach, Priv-CLIP aligns IMU sensor data with natural language activity descriptions in a shared embedding space, enabling few-shot detection of sensitive activities. When a privacy-sensitive activity is identified, the system uses a language-guided activity sanitizer and a motion generation module (IMU-GPT) to transform the original data into a privacy-compliant version that semantically resembles a non-sensitive activity. We evaluate PrivCLIP on multiple human activity recognition datasets and demonstrate that it significantly outperforms baseline methods in terms of both privacy protection and data utility. A growing number of smart devices, including wearables and smartphones, are equipped with sensors that enable applications in health monitoring, fitness tracking, and human activity recognition (HAR). Among these, inertial measurement units (IMUs) are particularly useful, as they capture fine-grained motion data that can be used to infer user behavior, physical condition, and mobility patterns. Typically, this sensor data is collected and transmitted to third-party cloud services for large-scale sensing and analytics. In many applications, online data transmission is desirable. Online tracking facilitates data sharing with peers, which enhances user engagement by providing timely feedback and positive reinforcement, which can be critical for sustained participation. However, outsourcing data processing to third-party providers raises significant privacy concerns.