arXiv subjects

Tong Wen

Publications and source records attributed to Tong Wen.

3 recordsLinked to original sources

arXiv2021

zk-Fabric, a Polylithic Syntax Zero Knowledge Joint Proof System

In this paper, we create a single-use and full syntax zero-knowledge proof system, a.k.a zk-Fabric. Comparing with zk-SNARKS and another variant zero-knowledge proofing system, zkBOO and it's variant zkBOO++. We present multiple new approaches on how to use partitioned garbled circuits to achieve a joint zero-knowledge proof system, with the benefits of less overhead and full syntax verification. zk-Fabric based on partitioned garbled circuits has the advantage of being versatile and single-use, meaning it can be applied to arbitrary circuits with more comprehensive statements, and it can achieve the non-interactivity among all participants. One of the protocols proposed within is used for creating a new kind of partitioned garbled circuits to match the comprehensive Boolean logical expression with multiple variables, we use the term "polythitic syntax" to refer to the context-based multiple variables in a comprehensive statement. We also designed a joint zero knowledge proof protocol that uses partitioned garbled circuits

cs.CR↗

arXiv2018

Computational Optimal Transport for 5G Massive C-RAN Device Association

The massive scale of future wireless networks will create computational bottlenecks in performance optimization. In this paper, we study the problem of connecting mobile traffic to Cloud RAN (C-RAN) stations. To balance station load, we steer the traffic by designing device association rules. The baseline association rule connects each device to the station with the strongest signal, which does not account for interference or traffic hot spots, and leads to load imbalances and performance deterioration. Instead, we can formulate an optimization problem to decide centrally the best association rule at each time instance. However, in practice this optimization has such high dimensions, that even linear programming solvers fail to solve. To address the challenge of massive connectivity, we propose an approach based on the theory of optimal transport, which studies the economical transfer of probability between two distributions. Our proposed methodology can further inspire scalable algorithms for massive optimization problems in wireless networks.

cs.NI↗

arXiv2018

Flexible and Scalable Deep Learning with MMLSpark

In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark. To achieve this, we have contributed Java Language bindings to the Cognitive Toolkit, and added several new components to the Spark ecosystem. In addition, we also integrate the popular image processing library OpenCV with Spark, and present a tool for the automated generation of PySpark wrappers from any SparkML estimator and use this tool to expose all work to the PySpark ecosystem. Finally, we provide a large library of tools for working and developing within the Spark ecosystem. We apply this work to the automated classification of Snow Leopards from camera trap images, and provide an end to end solution for the non-profit conservation organization, the Snow Leopard Trust.

cs.DC↗