Pair-then-Aggregate (PtA) introduces a programming paradigm and an automated parallel execution engine for large-scale secure multi-party (MPC) computations, drawing inspiration from the widely-used yet not explicitly defined Table-Generation-and-Look-up (TGL) pattern in privacy-preserving algorithm design. PtA offers an easy-to-use API and a versatile execution engine that harnesses various levels of parallelism and adapts to different MPC deployments, algorithms, and input sizes. Evaluations on a real-world MPC platform demonstrate significant enhancements in scalability, adaptability, and ease of programming. PtA can process one billion input elements with 3-23 lines of C++ code in 5-74 seconds. It outperforms state-of-the-art implementations in 91.4 % of 35 test cases, achieving up to a$12.4 \times$speedup with much less coding effort.11Our code is provided in https://github.com/Fannxy/Pair-then-Aggregate

Enabling various parties to share data enhances online fraud detection capabilities considering fraudsters tend to reuse resources attacking multiple platforms. Multi-party computation (MPC) techniques, such as secret sharing, offer potential privacy-preserving solutions but face efficiency challenges when handling large-scale data. This paper presents a novel approach, SecureFD (Secure Fraud Detector), aimed at detecting fraud in multi-party graph data, ensuring privacy, accuracy, and scalability. We propose a graph neural network EPR-GNN, which is MPC-friendly, as the base detector. Then we design a framework that allows multiple parties to train EPR-GNN collaboratively on secure sparse graphs in a privacy- preserving manner. The oblivious node embedding sharing protocol in the collaborative training procedure achieves up to a 45× speed-up, supporting over four million users compared to the naive solution. Additionally, we further reduce secure computation by locally pruning a significant number of non-suspicious users and selecting only the most valuable resources for sharing. Experiments on real datasets demonstrate that by securely integrating data from different parties, SecureFD achieves superior detection performance compared to state-of-the-art local detectors. And the local pruning greatly improves the scalability without compromising detection accuracies.

Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as "general'' as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen1, that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods.

Existing Byzantine fault tolerance (BFT) protocols face significant challenges in safety, scalability, throughput, and latency. We present a new BFT protocol, Gosig, for the consortium blockchains. Gosig guarantees safety even in asynchronous networks fully controlled by adversaries, by combining secret leader selection with multi-round voting. We co-design both the consensus protocol and the underlying gossip network to optimize performance. In particular, we adopt transmission pipelining to fully utilize the network bandwidth while use aggregated signature gossip to reduce the number of messages. These optimizations help Gosig to achieve unprecedented single-chain performance. On a public cloud testbed spanning multiple data centers consisting of 280 nodes across 14 cities on five continents, Gosig achieves over 15,000 transactions per second with 15.8-second confirmation time. When the system scales to 5,000 nodes, Gosig can still achieve 3,000 transactions per second with about 23.9-second confirmation time.

Data providers have a profound contribution to many fields such as finance, economy, and academia by serving people with both web-based and API-based query service of specialized data. Among the data users, there are data resellers who abuse the query APIs to retrieve and resell the data to make a profit, which harms the data provider's interests and causes copyright infringement. In this work, we define the "anti-data-reselling" problem and propose a new systematic method that combines feature engineering and machine learning models to provide a solution. We apply our method to a real query log of over 9,000 users with limited labels provided by a large financial data provider and get reasonable results, insightful observations, and real deployments.

Hardware failures have a big impact on the dependability of large-scale data centers. We present studies on over 290,000 hardware failure reports collected over the past four years from dozens of data centers with hundreds of thousands of servers. We examine the dataset statistically to discover failure characteristics along the temporal, spatial, product line and component dimensions. We specifically focus on the correlations among different failures, including batch and repeating failures, as well as the human operators' response to the failures. We reconfirm or extend findings from previous studies. We also find many new failure and recovery patterns that are the undesirable by-product of the state-of-the-art data center hardware and software design.

Given the high cost of large-scale data centers, an important design goal is to fully utilize available power resources to maximize the computing capacity. In this paper we present Ampere, a novel power management system for data centers to increase the computing capacity by over-provisioning the number of servers. Instead of doing power capping that degrades the performance of running jobs, we use a statistical control approach to implement dynamic power management by indirectly affecting the workload scheduling, which can enormously reduce the risk of power violations. Instead of being a part of the already over-complicated scheduler, Ampere only interacts with the scheduler with two basic APIs. Instead of power control on the rack level, we impose power constraint on the row level, which leads to more room for over provisioning.