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.

MOTIVATION: Quantitative structure-activity relationship (QSAR) and drug-target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery. RESULTS: We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery. AVAILABILITY AND IMPLEMENTATION: The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Privacy is a big hurdle for collaborative data mining across multiple parties. We present multi-party computation (MPC) framework designed for large-scale data mining tasks. PrivPy combines an easy-to-use and highly flexible Python programming interface with state-of-the-art secret-sharing-based MPC backend. With essential data types and operations (such as NumPy arrays and broadcasting), as well as automatic code-rewriting, programmers can write modern data mining algorithms conveniently in familiar Python. We demonstrate that we can support many real-world machine learning algorithms (e.g. logistic regression and convolutional neural networks) and large datasets (e.g. 5000-by-1-million matrix) with minimal algorithm porting effort.

With the ever increasing number of large scale Internet applications, inter data center (inter-DC) data transfers are becoming more and more common. Traditional inter-DC transfers suffers from both low-utilization and congestion, and traffic prediction is an important method to optimize these transfers. Inter-DC traffic is harder to predict than many other types of network traffic, because it is dominated by a few large applications. We propose a model that significantly reduces the prediction errors. In our model, we combine wavelet transform with artificial neural network (ANN) to improve prediction accuracy. Specifically, we explicitly add information of elephant flows, the least predictable yet dominating traffic in inter-DC network, into our prediction model. To reduce the amount of monitoring overhead for the elephant flow information, we added interpolation to fill in the unknown values in the elephant flows. We demonstrate that we can reduce prediction errors over existing methods by 5%~10%. Our prediction is already in production at Baidu, one of the largest Internet companies in China, helping reducing the peak network bandwidth.

With the ever increasing number of large scale Internet applications, inter-data-center (inter-DC) data transfers are becoming more and more common. Traditional inter-DC transfers suffer from both low utilization and congestion, and traffic prediction is an important method to optimize these transfers. Inter-DC traffic is harder to predict than many other types of network traffic because it is dominated by a few large applications. We propose a model that significantly reduces the prediction errors. In our model, we combine wavelet transform with artificial neural network to improve prediction accuracy. Specifically, we explicitly add information of sublink traffic and elephant flows, the least predictable yet dominating traffic in inter-DC network, into our prediction model. To reduce the amount of monitoring overhead for the elephant flow information, we add interpolation to fill in the unknown values in the elephant flows. We demonstrate that we can reduce prediction errors over existing methods by 5%~30%. Our prediction is in production as part of the traffic scheduling system at Baidu, one of the largest Internet companies in China, helping to reduce the peak network bandwidth.