It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven’t neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient’s personal information. This paper provides a novel split learning algorithm coined the term, “multi-site split learning”, which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consists of a number of clients and a centralized server. Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer. Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed. We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches. This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels.

This paper proposes ZenFS+, a new storage backend of RocksDB for small-zone ZNS SSD. RocksDB has complicated internal operations such as flush and compaction. Flush and compaction run in separate threads, and it is well known that they closely interact around the sstables. Due to the concurrent storage I/O between flush and compaction, ZenFS presents unsatisfactory performance for small-zone ZNS SSD. We believe that ZenFS+ presents how ZNS SSD can support the performance and isolation of modern key-value stores. Leveraging the ZNS SSD, ZenFS+ intelligently identifies the independent zones group (IZG), which reveals the device’s internal parallelism. With the IZG information, ZenFS+ effectively isolates the flush workloads of RocksDB from compaction. Besides, ZenFS+ spreads the sstables to multiple IZGs so that the storage write can leverage the hardware parallelism. ZenFS+ presents up to 4.8x higher storage throughput for write-intensive microbenchmark and stabilizing 99.9P tail latency by 1/51 from existing ZenFS. Further, ZenFS+ implements proactive garbage collection, and presents sustainable performance over longer lifespan of the device.

The ZNS (Zoned NameSpace) interface shifts much of the storage maintenance responsibility to the host from the underlying SSDs (Solid-State Drives). In addition, it opens a new opportunity to exploit the internal parallelism of SSDs at both hardware and software levels. By orchestrating the mapping between zones and SSD-internal resources and by controlling zone allocation among threads, ZNS SSDs provide a distinct performance trade-off between parallelism and isolation. To understand and explore the design space of ZNS SSDs, we present ConfZNS (Configurable ZNS), an easy-to-configure and timing-accurate emulator based on QEMU. ConfZNS allows users to investigate a variety of ZNS SSD's internal architecture and how it performs with existing host software. We validate the accuracy of ConfZNS using real ZNS SSDs and explore performance characteristics of different ZNS SSD designs with real-world applications such as RocksDB, F2FS, and Docker environment.

Mobile phones have evolved into complex systems as they have more and more new applications built-in. As a result, they are less reliable and less secure than before. Virtual Machine Monitors (VMM) or hypervisors have been introduced to help the reliability and security of mobile phones but the existing research does not completely address three issues critical to mobile phones: real-time support, resource limitation, and power efficiency. In this paper we propose building a new VMM called MobiVMM for mobile phones to deal with these issues. MobiVMM enables real-time support using priority based scheduling and a pseudo-polling mechanism. Resource and power efficiency is achieved through light-weight design and implementation, highly customized guest operating systems, and a virtual hardware abstraction layer. We present our design considerations and report some preliminary experimental results based on the OMAP 2430 development platform.