As a new design and manufacture paradigm, Cloud-Based Collaborative Design (CBCD) has motivated designers to outsource their product data and design computation onto the cloud service. Despite non-negligible benefits of CBCD, there are potential security threats for the outsourced product data, such as intellectual property, design intentions and private identity, which has become an interest point. This paper presents a novel secure product data exchange (PDE) in the processes of CBCD. Different from general cloud security mechanism, our method is content-based. We first show an outline of the collaborative scenario to describe the architecture of the proposed secure CBCD, in which a security mechanism is combined with the data exchange service to achieve secure PDE. Second, we present a novel grid-based geometric deformation method for the security mechanism with three processes: the original shapes of a source Computer Aided Design (CAD) model can be hidden by deforming the control grid; then the deformed grid can be exchanged to target system where a deformed target CAD model can be reconstructed; at last, the deformed target CAD model can be recovered to the original shape after recovering the deformed grid. Finally, typical CAD model tests demonstrate that our method can keep the sensitive information of source model and also maintain the same level of data exchange error.

中文翻译：

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用于基于云的协作设计的基于网格的安全产品数据交换

作为一种新的设计和制造范式，基于云的协同设计 (CBCD) 促使设计师将他们的产品数据和设计计算外包到云服务上。尽管 CBCD 的好处不可忽视，但外包产品数据存在潜在的安全威胁，如知识产权、设计意图和私人身份，这已成为一个兴趣点。本文提出了一种在 CBCD 过程中的新型安全产品数据交换 (PDE)。不同于一般的云安全机制，我们的方法是基于内容的。我们首先展示协作场景的轮廓来描述所提出的安全 CBCD 的架构，其中安全机制与数据交换服务相结合以实现安全 PDE。第二，我们提出了一种新的基于网格的安全机制几何变形方法，它具有三个过程：可以通过变形控制网格来隐藏源计算机辅助设计 (CAD) 模型的原始形状；然后可以将变形的网格交换到目标系统，在该系统中可以重建变形的目标CAD模型；最后，在恢复变形网格后，变形后的目标CAD模型可以恢复到原始形状。最后，典型的 CAD 模型测试表明，我们的方法可以保留源模型的敏感信息，并保持相同水平的数据交换误差。然后可以将变形的网格交换到目标系统，在该系统中可以重建变形的目标CAD模型；最后，在恢复变形网格后，变形后的目标CAD模型可以恢复到原始形状。最后，典型的 CAD 模型测试表明，我们的方法可以保留源模型的敏感信息，并保持相同水平的数据交换误差。然后可以将变形的网格交换到目标系统，在该系统中可以重建变形的目标CAD模型；最后，在恢复变形网格后，变形后的目标CAD模型可以恢复到原始形状。最后，典型的 CAD 模型测试表明，我们的方法可以保留源模型的敏感信息，并保持相同水平的数据交换误差。