Privacy-Preserving Production Process Parameter Exchange

被引:11
|
作者
Pennekamp, Jan [1 ]
Buchholz, Erik [1 ]
Lockner, Yannik [2 ]
Dahlmanns, Markus [1 ]
Xi, Tiandong [3 ]
Fey, Marcel [3 ]
Brecher, Christian [3 ]
Hopmann, Christian [2 ]
Wehrle, Klaus [1 ]
机构
[1] Rhein Westfal TH Aachen, Commun & Distributed Syst, Aachen, Germany
[2] Rhein Westfal TH Aachen, Inst Plast Proc, Aachen, Germany
[3] Rhein Westfal TH Aachen, Machine Tools & Prod Engn, Aachen, Germany
关键词
secure industrial collaboration; Bloom filter; oblivious transfer; Internet of Production; INJECTION-MOLDING PROCESS; ARTIFICIAL NEURAL-NETWORK; OPTIMIZATION; INTERNET; HYBRID; MODEL;
D O I
10.1145/3427228.3427248
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Nowadays, collaborations between industrial companies always go hand in hand with trust issues, i.e., exchanging valuable production data entails the risk of improper use of potentially sensitive information. Therefore, companies hesitate to offer their production data, e.g., process parameters that would allow other companies to establish new production lines faster, against a quid pro quo. Nevertheless, the expected benefits of industrial collaboration, data exchanges, and the utilization of external knowledge are significant. In this paper, we introduce our Bloom filter-based Parameter Exchange (BPE), which enables companies to exchange process parameters privacy-preservingly. We demonstrate the applicability of our platform based on two distinct real-world use cases: injection molding and machine tools. We show that BPE is both scalable and deployable for different needs to foster industrial collaborations. Thereby, we reward data-providing companies with payments while preserving their valuable data and reducing the risks of data leakage.
引用
收藏
页码:510 / 525
页数:16
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