Xin Chen

Zhongming Yu, Genghan Zhang, Hanxian Huang et al.

In recent years, Graph Neural Networks (GNNs) have ignited a surge of innovation, significantly enhancing the processing of geometric data structures such as graphs, point clouds, and meshes. As the domain continues to evolve, a series of frameworks and libraries are being developed to push GNN efficiency to new heights. While graph-centric libraries have achieved success in the past, the advent of efficient tensor compilers has highlighted the urgent need for tensor-centric libraries. Yet, efficient tensor-centric frameworks for GNNs remain scarce due to unique challenges and limitations encountered when implementing segment reduction in GNN contexts. We introduce GeoT, a cutting-edge tensor-centric library designed specifically for GNNs via efficient segment reduction. GeoT debuts innovative parallel algorithms that not only introduce new design principles but also expand the available design space. Importantly, GeoT is engineered for straightforward fusion within a computation graph, ensuring compatibility with contemporary tensor-centric machine learning frameworks and compilers. Setting a new performance benchmark, GeoT marks a considerable advancement by showcasing an average operator speedup of 1.80x and an end-to-end speedup of 1.68x.

Zhili Chen, Xin Chen

Due to the great development of secure multi-party computation, many practical secure computation schemes have been proposed. As an example, different secure auction mechanisms have been widely studied, which can protect bid privacy while satisfying various economic properties. However, as far as we know, none of them solve the secure computation problems for multiple data providers (e.g., secure cloud resource auctions) in the malicious security model. In this paper, we use the techniques of cut-and-choose and garbled circuits to propose a general secure computation framework for multiple data providers against malicious adversaries. Specifically, our framework checks input consistency with the cut-and-choose paradigm, conducts maliciously secure computations by running two independent garbled circuits, and verifies the correctness of output by comparing two versions of outputs. Theoretical analysis shows that our framework is secure against a malicious computation party, or a subset of malicious data providers. Taking secure cloud resource auctions as an example, we implement our framework. Extensive experimental evaluations show that the performance of the proposed framework is acceptable in practice.