A Byzantine Fault-Tolerant (BFT) protocol protects a distributed system from faulty participants. To provide both liveness and safety, many such protocols assume they are dealing with a partially-synchronous network, which will eventually stabilize after a global stabilization time (GST). In a real-world network environment, however, there is no such guarantee of bounded transmission time for network packets. For this reason, even if a BFT protocol is mathematically proven to achieve both liveness and safety, its overall performance is difficult to analyze theoretically, especially if there are bad network conditions or adversarial behaviors. Accordingly, we propose a simulator for evaluating the performance of BFT protocols under various network conditions and attacks, and we implement it to empirically compare the performance of eight representative protocols. Experiment results show that our simulator can simulate 16 times as many nodes as an existing simulator supports (512 vs. 32), and it is over 500 times faster when simulating 32 nodes (38 milliseconds vs. 19.4 seconds).

With the spread of IoT devices, various data about our lives are being collected, such as heart rate, physical activity, number of steps, pulse, oxygen intake, calorie consumption, etc. If these data can be analyzed, it will be possible to learn the signs of disease. However, it is dangerous for a person’s activity status to be managed on an external server with the view of privacy. To solve this problem, local differential privacy (LDP), which is a technique for randomly adding local noise to data, has been proposed. While ensuring privacy by LDP is certainly important, it degrades the usefulness of the analysis of data with added noise. In this paper, we propose a new mechanism to protect data privacy in both phases of training and testing based on LDP. We also make sure feasibility of our mechanism in two cases of breast cancer screening data and ionosphere data set.

The X-value arises in various contexts of system design. It often represents an unknown value or a don't-care value depending on the application. Verification of X-valued circuits is a crucial task but relatively unaddressed. The challenge of equivalence checking for X-valued circuits, named compatible equivalence checking, is posed in the 2020 ICCAD CAD Contest. In this paper, we present our winning method based on X-value preserving dual-rail encoding and incremental identification of compatible equivalence relation. Experimental results demonstrate the effectiveness of the proposed techniques and the outperformance of our approach in solving more cases than the commercial tool and the other teams among the top 3 of the contest.