Diversity-Aware Anonymization for Structured Health Data.

Patients' health data are captured by local hospital facilities, which has the potential for data analysis. However, due to privacy and legal concerns, local hospital facilities are unable to share the data with others which makes it difficult to apply data analysis and machine learning techniques over the health data. Analysis of such data across hospitals can provide valuable information to health professionals. Anonymization methods offer privacy-preserving solutions for sharing data for analysis purposes. In this paper, we propose a novel method for anonymizing and sharing data that addresses the record-linkage and attribute-linkage attack models. Our proposed method achieves anonymity by formulating and solving this problem as a constrained optimization problem which is based on the k-anonymity, l-diversity, and t-closeness privacy models. The proposed method has been evaluated with respect to the utility and privacy of data after anonymization in comparison to the original data.

Monitoring Motor Activity Data for Detecting Patients' Depression Using Data Augmentation and Privacy-Preserving Distributed Learning.

Wearable devices are currently being considered to collect personalized physiological information, which is lately being used to provide healthcare services to individuals. One application is detecting depression by utilization of motor activity signals collected by the ActiGraph wearable wristbands. However, to develop an accurate classification model, we require to use a sufficient volume of data from several subjects, taking the sensitivity of such data into account. Therefore, in this paper, we present an approach to extract classification models for predicting depression based on a new augmentation technique for motor activity data in a privacy-preserving fashion. We evaluate our approach against the state-of-the-art techniques and demonstrate its performance based on the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) Project.