标题： 以隐私保护人工智能工具赋能制造商：一种用于解决现实问题的隐私保护机器学习案例研究 显示英文标题

标题： Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

摘要： 小型和中型制造商需要创新的数据工具，但由于竞争和隐私问题，通常不希望将他们的专有数据与可能有兴趣帮助他们的研究人员共享。 本文介绍了一个隐私保护平台，制造商可以通过安全的方法将数据与研究人员安全共享，这样研究人员就可以创建创新工具来解决制造商的实际问题，然后将执行解决方案的工具提供回平台，供其他人使用，并保证隐私和保密性。 我们通过一个特定的用例来说明这个问题，该用例解决了食品晶体大规模制造中的一个重要问题，即质量控制依赖于图像分析工具。 在我们的研究之前，图像中的食品晶体是手动计数的，这需要大量且耗时的人工努力，但我们已经开发并部署了一种晶体分析工具，使这一过程更加迅速和准确。 该工具能够从显微镜图像中自动表征晶体粒径分布和数量，同时自动去除样品制备过程中产生的自然缺陷；还开发了一个机器学习模型，用于计数高分辨率的透明晶体和晶体团聚体，以协助这些工作。 然后，该算法通过一个基于网络的应用程序进行打包，通过原始的隐私保护平台进行安全处理，在工厂现场进行实际应用，使制造商可以使用它，同时保持其专有数据的安全性。 在展示了这一完整流程之后，也探讨了未来的发展方向。 显示英文摘要

摘要： Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execute solutions back onto the platform for others to use with privacy and confidentiality guarantees. We illustrate this problem through a particular use case which addresses an important problem in the large-scale manufacturing of food crystals, which is that quality control relies on image analysis tools. Previous to our research, food crystals in the images were manually counted, which required substantial and time-consuming human efforts, but we have developed and deployed a crystal analysis tool which makes this process both more rapid and accurate. The tool enables automatic characterization of the crystal size distribution and numbers from microscope images while the natural imperfections from the sample preparation are automatically removed; a machine learning model to count high resolution translucent crystals and agglomeration of crystals was also developed to aid in these efforts. The resulting algorithm was then packaged for real-world use on the factory floor via a web-based app secured through the originating privacy-preserving platform, allowing manufacturers to use it while keeping their proprietary data secure. After demonstrating this full process, future directions are also explored.