Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework
Ajesh Koyatan Chathoth, Shuhao Yu, Stephen Lee

TL;DR
PrivCLIP is a novel framework that enables user-controlled, dynamic privacy preservation in sensor data by using multimodal contrastive learning and language-guided data transformation, allowing few-shot adaptation to individual privacy preferences.
Contribution
This work introduces PrivCLIP, a flexible, user-controllable privacy-preserving sensing system that leverages multimodal contrastive learning and language-guided data sanitization for the first time.
Findings
PrivCLIP outperforms baseline methods in privacy protection.
The framework maintains high data utility for non-sensitive activities.
PrivCLIP adapts effectively with few-shot learning for user-specific privacy preferences.
Abstract
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 (black-listed), non-sensitive (white-listed), or neutral…
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