Privacy-Preserving Production Process Parameter Exchange

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.

[1]  Philipp Reinecke,et al.  Performance and Security Tradeoff , 2010, SFM.

[2]  Jan Rüth,et al.  Towards Executing Computer Vision Functionality on Programmable Network Devices , 2019, ENCP '19.

[3]  Eric Rescorla,et al.  The Transport Layer Security (TLS) Protocol Version 1.3 , 2018, RFC.

[4]  R Spina,et al.  OPTIMIZATION OF INJECTION MOLDED PARTS BY USING ANN-PSO APPROACH , 2006 .

[5]  Meiabadi Mohammad Saleh,et al.  Optimization of Plastic Injection Molding Process by Combination of Artificial Neural Network and Genetic Algorithm , 2013 .

[6]  Klaus Wehrle,et al.  Security Considerations for Collaborations in an Industrial IoT-based Lab of Labs , 2019, 2019 IEEE Global Conference on Internet of Things (GCIoT).

[7]  James P. Titus,et al.  Security and Privacy , 1967, 2022 IEEE Future Networks World Forum (FNWF).

[8]  Oded Goldreich,et al.  A randomized protocol for signing contracts , 1985, CACM.

[9]  Robert H. Deng,et al.  Encrypted data processing with Homomorphic Re-Encryption , 2017, Inf. Sci..

[10]  Andrew Kusiak,et al.  Smart manufacturing must embrace big data , 2017, Nature.

[11]  J. Pennekamp,et al.  BLOOM: BLoom filter based oblivious outsourced matchings , 2017, BMC Medical Genomics.

[12]  Rafail Ostrovsky,et al.  Public Key Encryption with Keyword Search , 2004, EUROCRYPT.

[13]  Liang Ma,et al.  Multiobjective optimization of injection molding process parameters based on Opt LHD, EBFNN, and MOPSO , 2015, The International Journal of Advanced Manufacturing Technology.

[14]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[15]  Gregory Kucherov,et al.  Using cascading Bloom filters to improve the memory usage for de Brujin graphs , 2013, Algorithms for Molecular Biology.

[16]  Christian Brecher,et al.  Towards an Infrastructure Enabling the Internet of Production , 2019, 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS).

[17]  Christian Brecher,et al.  Dataflow Challenges in an Internet of Production: A Security & Privacy Perspective , 2019, CPS-SPC@CCS.

[18]  Vladimir Kolesnikov,et al.  Efficient Batched Oblivious PRF with Applications to Private Set Intersection , 2016, CCS.

[19]  Hamid Mozaffari,et al.  Heterogeneous Private Information Retrieval , 2020, NDSS.

[20]  Tobias Meisen,et al.  Combined learning processes for injection moulding based on simulation and experimental data , 2019, PROCEEDINGS OF PPS-33 : The 33rd International Conference of the Polymer Processing Society – Conference Papers.

[21]  Peter Rindal,et al.  Malicious-Secure Private Set Intersection via Dual Execution , 2017, CCS.

[22]  Ali Vatankhah Barenji,et al.  Cloud-based manufacturing blockchain : secure knowledge sharing for injection mould redesign , 2018 .

[23]  Hrelja Marko,et al.  Turning Parameters Optimization Using Particle Swarm Optimization , 2014 .

[24]  Yehuda Lindell,et al.  More efficient oblivious transfer and extensions for faster secure computation , 2013, CCS.

[25]  Fanhuai Shi,et al.  Optimisation of Plastic Injection Moulding Process with Soft Computing , 2003 .

[26]  Christian Brecher,et al.  FactDAG: Formalizing Data Interoperability in an Internet of Production , 2020, IEEE Internet of Things Journal.

[27]  Stefan Behnel,et al.  Cython: The Best of Both Worlds , 2011, Computing in Science & Engineering.

[28]  Kristin E. Lauter,et al.  Private genome analysis through homomorphic encryption , 2015, BMC Medical Informatics and Decision Making.

[29]  Alejandro Alvarado-Iniesta,et al.  Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm , 2013 .

[30]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[31]  Craig Gentry,et al.  Private Database Queries Using Somewhat Homomorphic Encryption , 2013, ACNS.

[32]  F. Ramadhan,et al.  Inter-organizational trust and knowledge sharing model between manufacturer and supplier in the automotive industry , 2016, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[33]  Christian Brecher,et al.  Productivity Increase - Model-based optimisation of NC-controlled milling processes to reduce machining time and improve process quality , 2019 .

[34]  Zhicong Huang,et al.  Efficient and secure outsourcing of genomic data storage , 2017, BMC Medical Genomics.

[35]  Claudio Soriente,et al.  Size-Hiding in Private Set Intersection: Existential Results and Constructions , 2012, AFRICACRYPT.

[36]  Eyal Kushilevitz,et al.  Private information retrieval , 1998, JACM.

[37]  Frank Gens,et al.  Cloud Computing Benefits, risks and recommendations for information security , 2010 .

[38]  Eric Rescorla,et al.  The Transport Layer Security (TLS) Protocol Version 1.2 , 2008, RFC.

[39]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[40]  Zhipeng Cai,et al.  Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs , 2020, IEEE Journal on Selected Areas in Communications.

[41]  C. Jebaraj,et al.  Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO) , 2019, Measurement.

[42]  Wenhua Ye,et al.  Application of sliced inverse regression with fuzzy clustering for thermal error modeling of CNC machine tool , 2016 .

[43]  Dawn Xiaodong Song,et al.  Practical techniques for searches on encrypted data , 2000, Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000.

[44]  Emiliano De Cristofaro,et al.  Practical Private Set Intersection Protocols with Linear Complexity , 2010, Financial Cryptography.

[45]  Kuo-Ming Tsai,et al.  An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm , 2014, Journal of Intelligent Manufacturing.

[46]  Christian Brecher,et al.  Industrial Internet of Things and Cyber Manufacturing Systems , 2017 .

[47]  C. Bradai,et al.  Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation , 2019, The International Journal of Advanced Manufacturing Technology.

[48]  Kuo-Ming Tsai,et al.  Comparison of injection molding process windows for plastic lens established by artificial neural network and response surface methodology , 2015 .

[49]  Yingfeng Zhang,et al.  A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions , 2019, Journal of Cleaner Production.

[50]  Mohammadreza Sedighi,et al.  Optimisation of gate location based on weld line in plastic injection moulding using computer-aided engineering, artificial neural network, and genetic algorithm , 2017 .

[51]  Moni Naor,et al.  Efficient oblivious transfer protocols , 2001, SODA '01.

[52]  Michael O. Rabin,et al.  How To Exchange Secrets with Oblivious Transfer , 2005, IACR Cryptol. ePrint Arch..

[53]  Xiaoqian Jiang,et al.  FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption , 2015, BMC Medical Informatics and Decision Making.

[54]  Fang Liu,et al.  Security and Privacy in the Medical Internet of Things: A Review , 2018, Secur. Commun. Networks.

[55]  Wen-Chin Chen,et al.  An integrated parameter optimization system for MISO plastic injection molding , 2009 .

[56]  Vinod Vaikuntanathan,et al.  Can homomorphic encryption be practical? , 2011, CCSW '11.

[57]  Benny Pinkas,et al.  Faster Private Set Intersection Based on OT Extension , 2014, USENIX Security Symposium.

[58]  Ahmad-Reza Sadeghi,et al.  Security and privacy challenges in industrial Internet of Things , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[59]  Jens Hiller,et al.  Privacy-Preserving Remote Knowledge System , 2019, 2019 IEEE 27th International Conference on Network Protocols (ICNP).

[60]  Tobias Meisen,et al.  Transfer-Learning: Bridging the Gap between Real and Simulation Data for Machine Learning in Injection Molding , 2018 .

[61]  Donald Beaver,et al.  Correlated pseudorandomness and the complexity of private computations , 1996, STOC '96.

[62]  Peter Rindal,et al.  Improved Private Set Intersection Against Malicious Adversaries , 2017, EUROCRYPT.

[63]  D. Cica,et al.  Predictive model and optimization of processing parameters for plastic injection moulding , 2017 .

[64]  Mohammad Abdullah Al Faruque,et al.  Fix the leak! an information leakage aware secured cyber-physical manufacturing system , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[65]  Wen-Guey Tzeng,et al.  Efficient k-out-of-n Oblivious Transfer Schemes , 2005, J. Univers. Comput. Sci..

[66]  Salvatore J. Stolfo,et al.  Privacy-Preserving Sharing of Sensitive Information , 2010, IEEE Secur. Priv..

[67]  Yun Zhang,et al.  Intelligent methods for the process parameter determination of plastic injection molding , 2018, Frontiers of Mechanical Engineering.

[68]  Rakesh Nagi,et al.  Scheduling injection molding operations with multiple resource constraints and sequence dependent setup times and costs , 2005, Comput. Oper. Res..

[69]  Gerhard Lakemeyer,et al.  Interdisciplinary Data Driven Production Process Analysis for the Internet of Production , 2018 .

[70]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[71]  Prasad K. Yarlagadda,et al.  Prediction of processing parameters for injection moulding by using a hybrid neural network , 2001 .

[72]  Berend Denkena,et al.  Self-optimizing cutting process using learning process models , 2016 .

[73]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[74]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.