Privacy in Mobile Technology for Personal Healthcare

Information technology can improve the quality, efficiency, and cost of healthcare. In this survey, we examine the privacy requirements of \emphmobile\/ computing technologies that have the potential to transform healthcare. Such \emphmHealth\/ technology enables physicians to remotely monitor patients\u27 health, and enables individuals to manage their own health more easily. Despite these advantages, privacy is essential for any personal monitoring technology. Through an extensive survey of the literature, we develop a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discuss the technologies that could support privacy-sensitive mHealth systems. We end with a list of open research questions

PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives

Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becoming ubiquitous including in softwares for image recognition, speech recognition, speech synthesis, language translation, to name a few. he training of DNN architectures however is computationally expensive. Once the model is created, its use in the intended application - the inference task, is computationally heavy too and the inference needs to be fast for real time use. For obtaining high performance today, the code of Deep Learning (DL) primitives optimized for specific architectures by expert programmers exposed via libraries is the norm. However, given the constant emergence of new DNN architectures, creating hand optimized code is expensive, slow and is not scalable. To address this performance-productivity challenge, in this paper we present compiler algorithms to automatically generate high performance implementations of DL primitives that closely match the performance of hand optimized libraries. We develop novel data reuse analysis algorithms using the polyhedral model to derive efficient execution schedules automatically. In addition, because most DL primitives use some variant of matrix multiplication at their core, we develop a flexible framework where it is possible to plug in library implementations of the same in lieu of a subset of the loops. We show that such a hybrid compiler plus a minimal library-use approach results in state-of-the-art performance. We develop compiler algorithms to also perform operator fusions that reduce data movement through the memory hierarchy of the computer system.Comment: arXiv admin note: substantial text overlap with arXiv:2002.0214