Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it can discover insights behind real-world data, may not be feasible for many data-driven industry applications in light of such regulations. A new machine learning method, coined by Google as Federated Learning (FL) enables multiple participants to train a machine learning model collectively without directly exchanging data. However, recent studies have shown that there is still a possibility to exploit the shared models to extract personal or confidential data. In this paper, we propose to adopt Multi-Party Computation (MPC) to achieve privacy-preserving model aggregation for FL. The MPC-enabled model aggregation in a peer-to-peer manner incurs high communication overhead with low scalability. To address this problem, the authors proposed to develop a two-phase mechanism by 1) electing a small committee and 2) providing MPC-enabled model aggregation service to a larger number of participants through the committee. The MPC-enabled FL framework has been integrated in an IoT platform for smart manufacturing. It enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy, model accuracy vis-a`-vis traditional machine learning methods and execution efficiency in terms of communication cost and execution time.

In this study, we initiate a cyberinfrastructure solution by synergizing both the blockchain and Internet of Things (IoT) technologies for transportation insurance. The insurance premium related services are encapsulated in “on-chain” chaincodes to perform over the facts on vehicle's trip and driver's behavior, which are deduced through “off-chain” analytic services using the sensing data collected from vehicles' on-board sensors. A hybrid scheme coordinating both the permissioned (Hyperledger) and public (Ethereum) blockchains is proposed to exploit their respective capabilities in terms of high transaction throughput and built-in cryptocurrency. A working prototype platform is implemented with a basic premium calculation model. The prototype system is deployed across Amazon Web Services (AWSs) cloud in a real-world Internet environment. A comprehensive performance study from the aspects of throughput, latency, and resource usage under different configurations is presented to show the solution's feasibility. The design practice and research findings are concluded in consort with the experience gained for further enhancing the proposed solution and extending the functional features such as a more realistic insurance policy to be applied in generic vehicle insurance applications.

Secure Multi-Party Computation (MPC) is a promising privacy-preserving technology to enable multiple trustless parties to compute a function jointly without revealing private inputs to each other. With the fast development of MPC protocols, software implementation, and underlying computation infrastructure, MPC has developed from purely theoretical interest to tangible platform implementations for real-world applications. In this paper, we investigate multiple mechanisms to design efficient MPC algorithms by avoiding costly MPC operations and leveraging parallel operations. In order to speed up database table searching, a machine learning-based approach is proposed to completely avoid equality-check operations, playing the trade-off between efficiency and accuracy. According to our experimental results, a well-designed MPC algorithm could improve performance and scalability significantly, and thus make MPC technology practicable.

The byte-addressable non-volatile memory (NVM) is a promising medium for data storage. NVM-oriented file systems have been designed to explore NVM's performance potential. Meanwhile, applications may write considerable duplicate data. For NVM, a removal of duplicate data can promote space efficiency, improve write endurance, and potentially improve the performance by avoidance of repeatedly writing the same data. However, we have observed severe performance degradations when implementing a state-of-the-art inline deduplication algorithm in an NVM-oriented file system. A quantitative analysis reveals that, with NVM, 1) the conventional way to manage deduplication metadata for block devices, particularly in light of consistency, is inefficient, and, 2) the performance with deduplication becomes more subject to fingerprint calculations. We hence propose a deduplication algorithm called NV-Dedup. NV-Dedup manages deduplication metadata in a fine-grained, CPU and NVM-favored way, and preserves the metadata consistency with a lightweight transactional scheme. It also does workload-adaptive fingerprinting based on an analytical model and a transition scheme among fingerprinting methods to reduce calculation penalties. We have built a prototype of NV-Dedup in the Persistent Memory File System (PMFS). Experiments show that, NV-Dedup not only substantially saves NVM space, but also boosts the performance of PMFS by up to 2.1x.

Byte-addressable non-volatile memory (NVM) is going to reshape conventional computer systems. With advantages of low latency, byte-addressability, and non-volatility, NVM can be directly put on the memory bus to replace DRAM. As a result, both system and application softwares have to be adjusted to perceive the fact that the persistent layer moves up to the memory. However, most of the current in-memory data structures will be problematic with consistency issues if not well tuned with NVM. This article places emphasis on an important in-memory structure that is widely used in computer systems, i.e., the Red/Black-tree (RB-tree). Since it has a long and complicated update process, the RB-tree is prone to inconsistency problems with NVM. This article presents an NVM-compatible consistent RB-tree with a new technique named cascade-versioning . The proposed RB-tree (i) is all-time consistent and scalable and (ii) needs no recovery procedure after system crashes. Experiment results show that the RB-tree for NVM not only achieves the aim of consistency with insignificant spatial overhead but also yields comparable performance to an ordinary volatile RB-tree.

The byte-addressable non-volatile memory (NVM) is new promising storage medium. Compared to NAND flash memory, the next-generation NVM not only preserves the durability of stored data but has much shorter access latencies. An architect can utilize the fast and persistent NVM as an external disk cache. Regarding the system's crash consistency, a prevalent journaling file system needs to run atop an NVM disk cache. However, the performance is severely impaired by redundant efforts in achieving crash consistency in both file system and disk cache. Therefore, we propose a new mechanism called transactional NVM disk cache (Tinca). In brief, Tinca jointly guarantees consistency of file system and disk cache and removes the performance penalty of file system journaling with a lightweight transaction scheme. Evaluations confirm that Tinca significantly outperforms state-of-the-art design by up to 2.5X in local and cluster tests without causing any inconsistency issue.