Year: 2016
Type: conference-paper
Abstract: Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) - OVERALL, CSR: Large: Collaborative Research: Enabling Privacy-Utility Trade-Offs in Pervasive Computing Systems, G3229617247, Eliminating Tobacco-Related Disparities amount African American Smokers, G3632283481, G4189997643, G5255579673, Predicting Smoking Abstinence via Mobile Monitoring of Stress and Social Context, Socioeconomic status, stress, and smoking cessation, SHB: Type I (EXP): Collaborative Research: EasySense: Contact-less Physiological Sensing in the Mobile Environment Using Compressive Radio Frequency Probes, G936678091
OpenAlex ID: W2518265242