D&D: A Distributed and Disposable Approach to Privacy Preserving Data Analytics in UserCentric Healthcare Benefiting from the modern information and communication technologies, user centricity has become a clear evolution trend in healthcare. Unfortunately, given the high sensitivity of health data and the uncertainty in user environments, usercentric healthcare systems inevitably suffer from more frequent privacy threats, not to mention that technologies and business of data exploitation have generally outpaced the current privacy regulations and laws. Although there exist welldefined privacy preserving mechanisms, such as Data Encryption, Data Perturbation, and Deidentification, they have been considered inadequate to address the diverse privacy challenges in big healthcare data analytics. Our argument is that, before considering any sophisticated mechanism, practitioners should first try to imitate human memoryu0027s forgetting process as an intrinsic privacy preserving strategy in usercentric healthcare. Technically, we implement this strategy by changing traditional data analytics routines into a distributed and disposable manner, so as to naturally exclude the data ownersu0027 sensitive information. The technical implementation essentially acts as a concrete HowTo solution to satisfying a fundamental principle of privacy law, i.e. data minimization. We have initially applied our work to a smart bed project for sleep quality analytics, and received positive feedback on the effectiveness of privacy preservation in suitable homecare scenarios.

PrivacyPreserving Deep Learning for Enabling Big Edge Data Analytics in Internet of Things Big data analytics are pervasive in dataintensive systems and applications using machine learning and deep learning. IoT sensor devices are generating all types of big data, including structured (e.g., tables) and unstructured data (e.g., text and image), which are beyond the processing power of humans. However, how to conduct big data analysis on IoT data without compromising IoT privacy is still an open problem. IoT shall leverage powerful learning techniques to automatically learn patterns such as similarities, correlations and abnormalities from big sensing data in a privacypreserving manner. To make this happen, we first examine distributed learning techniques that are suitable for IoT architectures. We then propose a privacypreserving distributed learning framework with a novel dynamic deep learning mechanism to extract patterns and learn knowledge from IoT data. Simulations are performed to show the effectiveness and efficiency of our solution.