Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry. They have achieved remarkable success in numerous machine learning competitions and practical applications. Secure Multi-Party Computation (MPC) allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Gibbon, a secure two-party GBDT training framework on a vertically split dataset, where two data owners each hold different features of the same data samples. Compared with the state-of-the-art Squirrel (USENIX'Sec 2023), for most parameter settings, Gibbon achieves 2×-4× reduction in running time and 2×-3× reduction in communication.

Accurate extrinsic calibration of LiDAR and camera is crucial in multimodal perception systems. However, existing calibration methods focus on the calibration between a single camera and a LiDAR, overlooking the capability of using multi-view perspectives to eliminate defects in single-view images. In this work, we propose a novel automatic LiDAR-camera calibration method for sensor systems equipped with multiple cameras and one LiDAR. First, we propose a grid-level method to establish the correspondences between images and point clouds, allowing the pixel coordinate of the projected point cloud to be continuously differentiable. Then, we apply a multi-view joint semantic and intensity consistency (MJSIC) score to evaluate the quality of the extrinsic parameters. Finally, we propose an adaptive score gradient optimization (SGO) algorithm based on the gradient of the MJSIC score to compute the extrinsic parameters with minimal computational resources. To verify the benefits of our method, we conducted experiments on the KITTI odometry benchmark dataset. The experimental results demonstrate the accuracy and robustness of the proposed approach, as well as its superiority in terms of time efficiency.

The input vector-oriented reliability estimation of sequential circuits plays an important role in predicting their reliability boundaries and identifying their reliability-critical gates. This article presents an input vector-oriented programmable method based on fanout-source tracking and reduction for the reliability evaluation of sequential circuits. In the proposed method, fanout-source tracking is introduced to track fanout sources of a computing node to determine the fanout sources affecting the node output signals. An iterative reduction method is presented to eliminate duplicate calculations caused by fanout reconvergences without reducing the accuracy. A dynamic fanout relevance-keeping-based calculation method is used to approximate the trend probability vector of low-priority nodes outputting “0” and “1” to accelerate the calculations at a small accuracy loss. A complexity-accuracy tradeoff method based on a programmable fanout source length is designed to facilitate reasonable calculations as needed. Experimental results on large-scale circuits show that the average relative error of the proposed method is 1.01% with Monte Carlo (MC) as a reference. Moreover, the proposed method is 4,308.13 times faster than the MC on average, but its average memory cost is 3.40 higher than that of the MC model. Compared with similar methods, the proposed method not only is suitable for large-scale circuits and performs better in accuracy, but also enables programmable computation to dynamically balance the tradeoff between accuracy and speed as actual needed, resulting in better applicability and scalability.

The world has observed an increasing trend in the development of Privacy-Preserving Machine Learning (PPML) for cross-silo collaborative model training over sensitive data. As the first essential step of cross-silo PPML, it is critical that the parties can align their dataset with privacy assurance, i.e.,private data join. However, the existing private data join methods typically leak the ID information in the dataset intersection, which often raises privacy concerns. In this work, we propose iPrivJoin: a novel framework of ID-private data join for PPML. Compared with naively using circuit-based Private Set Intersection (circuit-PSI) for data join, the proposed framework has two advantages. (i) data volume reduction. iPrivJoin utilizes oblivious shuffle to securely trim off the redundant data that is outside the intersection, while the entire dataset needs to be carried to further process in the circuit-PSI based approach. (ii) efficiency improvement. iPrivJoin introduces a new private encoding technique to avoid the expensive circuit evaluation that is needed in circuit-PSI. As a result, compared with directly using circuit-PSI, PPML with iPrivJoin enjoys approximately 3× of speedup. Moreover, we propose a new oblivious shuffle protocol, which may be of independent interest. It achieves 1.44× of speedup to the state-of-the-art in the real-world WAN network setting.