skip to main content
10.1145/3427228.3427248acmotherconferencesArticle/Chapter ViewAbstractPublication PagesacsacConference Proceedingsconference-collections
research-article

Privacy-Preserving Production Process Parameter Exchange

Published:08 December 2020Publication History

ABSTRACT

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.

References

  1. Alejandro Alvarado Iniesta, Jorge L García Alcaraz, and ManuelIván Rodríguez Borbón. 2013. Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm. Revista Facultad de Ingeniería Universidad de Antioquia67 (2013), 43–51.Google ScholarGoogle Scholar
  2. Gilad Asharov, Yehuda Lindell, Thomas Schneider, and Michael Zohner. 2013. More Efficient Oblivious Transfer and Extensions for Faster Secure Computation. In Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security (CCS ’13). ACM, 535–548. https://doi.org/10.1145/2508859.2516738Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Donald Beaver. 1996. Correlated Pseudorandomness and the Complexity of Private Computations. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC ’96). ACM, 479–488. https://doi.org/10.1145/237814.237996Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Stefan Behnel, Robert Bradshaw, Craig Citro, Lisandro Dalcin, Dag Sverre Seljebotn, and Kurt Smith. 2011. Cython: The Best of Both Worlds. Computing in Science & Engineering 13, 2 (2011), 31–39. https://doi.org/10.1109/MCSE.2010.118Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. David Belson. 2017. State of the Internet Report — Q1 2017 report. Technical Report. Akamai Technologies.Google ScholarGoogle Scholar
  6. R. Joseph Bensingh, Rajendra Machavaram, Sadayan Rajendra Boopathy, and Chidambaram Jebaraj. 2019. Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO). Measurement 134(2019), 359–374. https://doi.org/10.1016/j.measurement.2018.10.066Google ScholarGoogle ScholarCross RefCross Ref
  7. Burton H. Bloom. 1970. Space/Time Trade-Offs in Hash Coding with Allowable Errors. Commun. ACM 13, 7 (1970), 422–426. https://doi.org/10.1145/362686.362692Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Dan Boneh, Giovanni Di Crescenzo, Rafail Ostrovsky, and Giuseppe Persiano. 2004. Public Key Encryption with Keyword Search. In Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT ’04). Springer, 506–522. https://doi.org/10.1007/978-3-540-24676-3_30Google ScholarGoogle ScholarCross RefCross Ref
  9. Dan Boneh, Craig Gentry, Shai Halevi, Frank Wang, and David J. Wu. 2013. Private Database Queries Using Somewhat Homomorphic Encryption. In Proceedings of the 11th International Conference on Applied Cryptography and Network Security (ACNS ’13). Springer, 102–118. https://doi.org/10.1007/978-3-642-38980-1_7Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Rainer Bourdon, Andreas Hellmann, Jan-Bernd Schreckenberg, and Ralf Schwegmann. 2010. Sind Wechselwirkungen simulierbar? Prozessoptimierung beim Spritzgießen mit statistischer Versuchsplanung. Kunststoffe 10(2010), 526.Google ScholarGoogle Scholar
  11. Rainer Bourdon, Andreas Hellmann, Jan-Bernd Schreckenberg, and Ralf Schwegmann. 2012. Standardized optimization of process and quality by DOE methods — a short manual for injection molding in practice. Journal of Plastics Technology 8, 5 (2012), 525–549.Google ScholarGoogle Scholar
  12. Christian Brecher, Marian Wiesch, and Frederik Wellmann. 2019. Productivity Increase – Model-based optimisation of NC-controlled milling processes to reduce machining time and improve process quality. IFAC-PapersOnLine 52, 13 (2019), 1803–1807. https://doi.org/10.1016/j.ifacol.2019.11.463Google ScholarGoogle ScholarCross RefCross Ref
  13. Daniele Catteddu. 2010. Cloud Computing: Benefits, Risks and Recommendations for Information Security. In Proceedings of the Iberic Web Application Security Conference (IBWAS ’10). Springer. https://doi.org/10.1007/978-3-642-16120-9_9Google ScholarGoogle ScholarCross RefCross Ref
  14. Ceresana. 2016. Plastic Injection Market Report. Technical Report. Ceresana.Google ScholarGoogle Scholar
  15. Wen-Chin Chen, Min-Wen Wang, Chen-Tai Chen, and Gong-Loung Fu. 2009. An integrated parameter optimization system for MISO plastic injection molding. The International Journal of Advanced Manufacturing Technology 44, 5–6(2009), 501–511. https://doi.org/10.1007/s00170-008-1843-4Google ScholarGoogle ScholarCross RefCross Ref
  16. Sujit Rokka Chhetri, Sina Faezi, and Mohammad Abdullah Al Faruque. 2017. Fix the Leak! An Information Leakage Aware Secured Cyber-Physical Manufacturing System. In Design, Automation & Test in Europe Conference & Exhibition (DATE ’17). IEEE, 1408–1413. https://doi.org/10.23919/DATE.2017.7927213Google ScholarGoogle ScholarCross RefCross Ref
  17. Benny Chor, Oded Goldreich, Eyal Kushilevitz, and Madhu Sudan. 1995. Private Information Retrieval. In Proceedings of IEEE 36th Annual Foundations of Computer Science (FOCS ’95). IEEE, 41–50. https://doi.org/10.1109/SFCS.1995.492461Google ScholarGoogle ScholarCross RefCross Ref
  18. Cheng-Kang Chu and Wen-Guey Tzeng. 2005. Efficient k-Out-of-n Oblivious Transfer Schemes with Adaptive and Non-adaptive Queries. In Proceedings of the 8th International Workshop on Theory and Practice in Public Key Cryptography (PKC ’05). Springer, 172–183. https://doi.org/10.1007/978-3-540-30580-4_12Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Cisco. 2020. Cisco Annual Internet Report (2018–2023) White Paper. White Paper. Cisco.Google ScholarGoogle Scholar
  20. Li Da Xu, Wu He, and Shancang Li. 2014. Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics 10, 4 (2014), 2233–2243. https://doi.org/10.1109/TII.2014.2300753Google ScholarGoogle ScholarCross RefCross Ref
  21. Markus Dahlmanns, Chris Dax, Roman Matzutt, Jan Pennekamp, Jens Hiller, and Klaus Wehrle. 2019. Privacy-Preserving Remote Knowledge System. In Proceedings of the 2019 IEEE 27th International Conference on Network Protocols (ICNP ’19). IEEE. https://doi.org/10.1109/ICNP.2019.8888121Google ScholarGoogle ScholarCross RefCross Ref
  22. Paolo D’Arco, María Isabel González Vasco, Angel L. Pérez del Pozo, and Claudio Soriente. 2012. Size-Hiding in Private Set Intersection: Existential Results and Constructions. In Proceedings of the 5th International Conference on Cryptology in Africa (AFRICACRYPT ’12). Springer, 378–394. https://doi.org/10.1007/978-3-642-31410-0_23Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Satyaki Ghosh Dastidar and Rakesh Nagi. 2005. Scheduling injection molding operations with multiple resource constraints and sequence dependent setup times and costs. Computers & Operations Research 32, 11 (2005), 2987–3005. https://doi.org/10.1016/j.cor.2004.04.012Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Emiliano De Cristofaro, Yanbin Lu, and Gene Tsudik. 2010. Privacy-preserving Sharing of Sensitive Information. Cryptology ePrint Archive 2010/471.Google ScholarGoogle Scholar
  25. Emiliano De Cristofaro and Gene Tsudik. 2010. Practical Private Set Intersection Protocols With Linear Complexity. In Proceedings of the 14th International Conference on Financial Cryptography and Data Security (FC ’10). Springer, 143–159. https://doi.org/10.1007/978-3-642-14577-3_13Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Berend Denkena, Marc-André Dittrich, and Florian Uhlich. 2016. Self-optimizing Cutting Process Using Learning Process Models. Procedia Technology 26(2016), 221–226. https://doi.org/10.1016/j.protcy.2016.08.030Google ScholarGoogle ScholarCross RefCross Ref
  27. Tim Dierks and Eric Rescorla. 2018. The Transport Layer Security (TLS) Protocol Version 1.2. IETF RFC 5246.Google ScholarGoogle Scholar
  28. Wenxiu Ding, Zheng Yan, and Robert H. Deng. 2017. Encrypted data processing with Homomorphic Re-Encryption. Information Sciences 409–410 (2017), 35–55. https://doi.org/10.1016/j.ins.2017.05.004Google ScholarGoogle ScholarCross RefCross Ref
  29. Shimon Even, Oded Goldreich, and Abraham Lempel. 1985. A Randomized Protocol for Signing Contracts. Commun. ACM 28, 6 (1985), 637–647. https://doi.org/10.1145/3812.3818Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Huang Gao, Yun Zhang, Xundao Zhou, and Dequn Li. 2018. Intelligent Methods for the Process Parameter Determination of Plastic Injection Molding. Frontiers of Mechanical Engineering 13, 1 (2018), 85–95. https://doi.org/10.1007/s11465-018-0491-0Google ScholarGoogle ScholarCross RefCross Ref
  31. Craig Gentry. 2009. Fully Homomorphic Encryption Using Ideal Lattices. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing (STOC ’09). ACM, 169–178. https://doi.org/10.1145/1536414.1536440Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. René Glebke, Johannes Krude, Ike Kunze, Jan Rüth, Felix Senger, and Klaus Wehrle. 2019. Towards Executing Computer Vision Functionality on Programmable Network Devices. In Proceedings of the 1st ACM CoNEXT Workshop on Emerging in-Network Computing Paradigms (ENCP ’19). ACM, 15–20. https://doi.org/10.1145/3359993.3366646Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Lars Gleim, Jan Pennekamp, Martin Liebenberg, Melanie Buchsbaum, Philipp Niemietz, Simon Knape, Alexander Epple, Simon Storms, Daniel Trauth, Thomas Bergs, Christian Brecher, Stefan Decker, Gerhard Lakemeyer, and Klaus Wehrle. 2020. FactDAG: Formalizing Data Interoperability in an Internet of Production. IEEE Internet of Things Journal 7, 4 (2020), 3243–3253. https://doi.org/10.1109/JIOT.2020.2966402Google ScholarGoogle ScholarCross RefCross Ref
  34. Fatma Hentati, Ismail Hadriche, Neila Masmoudi, and Chedly Bradai. 2019. Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation. The International Journal of Advanced Manufacturing Technology 104, 9–12(2019), 4353–4363. https://doi.org/10.1007/s00170-019-04283-zGoogle ScholarGoogle ScholarCross RefCross Ref
  35. Tsuyoshi Hombashi. 2016. Tcconfig. https://github.com/thombashi/tcconfig.Google ScholarGoogle Scholar
  36. Christian Hopmann, Pascal Bibow, Thomas Kosthorst, and Yannik Lockner. 2020. Process setup in injection moulding by Human-Machine-Interfaces and AI. In Proceedings of the 30th International Colloquium Plastics Technology.Google ScholarGoogle Scholar
  37. Christian Hopmann and Julian Heinisch. 2018. Injection Molding Setup by Means of Machine Learning Based on Simulation and Experimental Data. In Proceedings of the 76th SPE Annual Technical Conference and Tradeshow (ANTEC ’18). Society of Plastics Engineers, 269–274.Google ScholarGoogle Scholar
  38. Christian Hopmann, Sabina Jeschke, Tobias Meisen, Thomas Thiele, Hasan Tercan, Martin Liebenberg, Julian Heinisch, and Matthias Theunissen. 2019. Combined learning processes for injection moulding based on simulation and experimental data. In Proceedings of the 33rd Polymer Processing Society Annual Meeting (PPS ’17), Vol. 2139. AIP, 152–156. https://doi.org/10.1063/1.5121656Google ScholarGoogle ScholarCross RefCross Ref
  39. Van Jacobson, Craig Leres, and Steven McCanne. 1988. TCPDUMP/LIBPCAP public repository. https://www.tcpdump.org/.Google ScholarGoogle Scholar
  40. Sabina Jeschke, Christian Brecher, Tobias Meisen, Denis Özdemir, and Tim Eschert. 2017. Industrial Internet of Things and Cyber Manufacturing Systems. Springer. https://doi.org/10.1007/978-3-319-42559-7_1Google ScholarGoogle ScholarCross RefCross Ref
  41. Miran Kim and Kristin Lauter. 2015. Private genome analysis through homomorphic encryption. BMC Medical Informatics and Decision Making 15 (Suppl 5) (2015). https://doi.org/10.1186/1472-6947-15-S5-S3Google ScholarGoogle ScholarCross RefCross Ref
  42. Vladimir Kolesnikov, Ranjit Kumaresan, Mike Rosulek, and Ni Trieu. 2016. Efficient Batched Oblivious PRF with Applications to Private Set Intersection. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16). ACM, 818–829. https://doi.org/10.1145/2976749.2978381Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Davorin Kramar and Djordje Cica. 2017. Predictive model and optimization of processing parameters for plastic injection moulding. Materials and Technology 51, 4 (2017), 597–602. https://doi.org/10.17222/mit.2016.129Google ScholarGoogle ScholarCross RefCross Ref
  44. Andrew Kusiak. 2017. Smart manufacturing must embrace big data. Nature 544, 7648 (2017), 23–25. https://doi.org/10.1038/544023aGoogle ScholarGoogle ScholarCross RefCross Ref
  45. Zhi Li, Layne Liu, Ali Vatankhah Barenji, and Waiming Wang. 2018. Cloud-based Manufacturing Blockchain: Secure Knowledge Sharing for Injection Mould Redesign. Procedia CIRP 72, 1 (2018), 961–966. https://doi.org/10.1016/j.procir.2018.03.004Google ScholarGoogle ScholarCross RefCross Ref
  46. Hrelja Marko, Klancnik Simon, Irgolic Tomaz, Paulic Matej, Balic Joze, and Brezocnik Miran. 2014. Turning Parameters Optimization Using Particle Swarm Optimization. Procedia Engineering 69(2014), 670–677. https://doi.org/10.1016/j.proeng.2014.03.041Google ScholarGoogle ScholarCross RefCross Ref
  47. Mohammad Saleh Meiabadi, Abbas Vafaeesefat, and Fatemeh Sharifi. 2013. Optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm. Journal of Optimization in Industrial Engineering 6, 13(2013), 49–54.Google ScholarGoogle Scholar
  48. Richard Meyes, Hasan Tercan, Thomas Thiele, Alexander Krämer, Julian Heinisch, Martin Liebenberg, Gerhard Hirt, Christian Hopmann, Gerhard Lakemeyer, Tobias Meisen, and Sabina Jeschke. 2018. Interdisciplinary Data Driven Production Process Analysis for the Internet of Production. Procedia Manufacturing 26 (2018), 1065–1076. https://doi.org/10.1016/j.promfg.2018.07.143Google ScholarGoogle ScholarCross RefCross Ref
  49. Hamid Mozaffari and Amir Houmansadr. 2020. Heterogeneous Private Information Retrieval. In Proceedings of the 28th Annual Network and Distributed System Security Symposium (NDSS ’20). Internet Society.Google ScholarGoogle ScholarCross RefCross Ref
  50. Michael Naehrig, Kristin Lauter, and Vinod Vaikuntanathan. 2011. Can Homomorphic Encryption Be Practical?. In Proceedings of the 3rd ACM Workshop on Cloud Computing Security Workshop (CCSW ’11). ACM, 113–124. https://doi.org/10.1145/2046660.2046682Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Moni Naor, Benny Pinkas, and Benny Pinkas. 2001. Efficient Oblivious Transfer Protocols. In Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’01). SIAM, 448–457.Google ScholarGoogle Scholar
  52. Tim A. Osswald, Lih-Sheng Turng, and Paul J. Gramann. 2007. Injection Molding Handbook(2nd ed.). Carl Hanser.Google ScholarGoogle Scholar
  53. Sinno Jialin Pan and Qiang Yang. 2009. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 10(2009), 1345–1359. https://doi.org/10.1109/TKDE.2009.191Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Jan Pennekamp, Markus Dahlmanns, Lars Gleim, Stefan Decker, and Klaus Wehrle. 2019. Security Considerations for Collaborations in an Industrial IoT-based Lab of Labs. In Proceedings of the 3rd IEEE Global Conference on Internet of Things (GCIoT ’19). IEEE. https://doi.org/10.1109/GCIoT47977.2019.9058413Google ScholarGoogle ScholarCross RefCross Ref
  55. Jan Pennekamp, René Glebke, Martin Henze, Tobias Meisen, Christoph Quix, Rihan Hai, Lars Gleim, Philipp Niemietz, Maximilian Rudack, Simon Knape, Alexander Epple, Daniel Trauth, Uwe Vroomen, Thomas Bergs, Christian Brecher, Andreas Bührig-Polaczek, Matthias Jarke, and Klaus Wehrle. 2019. Towards an Infrastructure Enabling the Internet of Production. In Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS ’19). IEEE, 31–37. https://doi.org/10.1109/ICPHYS.2019.8780276Google ScholarGoogle ScholarCross RefCross Ref
  56. Jan Pennekamp, Martin Henze, Simo Schmidt, Philipp Niemietz, Marcel Fey, Daniel Trauth, Thomas Bergs, Christian Brecher, and Klaus Wehrle. 2019. Dataflow Challenges in an Internet of Production: A Security & Privacy Perspective. In Proceedings of the ACM Workshop on Cyber-Physical Systems Security & Privacy (CPS-SPC ’19). ACM, 27–38. https://doi.org/10.1145/3338499.3357357Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Benny Pinkas, Thomas Schneider, and Michael Zohner. 2014. Faster Private Set Intersection Based on OT Extension. In Proceedings of the 23rd USENIX Conference on Security Symposium (SEC ’14). USENIX Association, 797–812.Google ScholarGoogle Scholar
  58. PlasticsEurope. 2019. Geschäftsbericht 2018. Technical Report. PlasticsEurope Deutschland e.V.Google ScholarGoogle Scholar
  59. Sinha Prashant. 2016. pybloomfiltermmap3. https://github.com/prashnts/pybloomfiltermmap3.Google ScholarGoogle Scholar
  60. Michael O. Rabin. 2005. How To Exchange Secrets with Oblivious Transfer. Cryptology ePrint Archive 2005/187.Google ScholarGoogle Scholar
  61. Fadillah Ramadhan and T. M. A. Ari Samadhi. 2016. Inter-Organizational Trust and Knowledge Sharing Model Between Manufacturer and Supplier in the Automotive Industry. In Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM ’16). IEEE, 856–860. https://doi.org/10.1109/IEEM.2016.7797998Google ScholarGoogle ScholarCross RefCross Ref
  62. Shan Ren, Yingfeng Zhang, Yang Liu, Tomohiko Sakao, Donald Huisingh, and Cecilia MVB Almeida. 2019. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production 210 (2019), 1343–1365. https://doi.org/10.1016/j.jclepro.2018.11.025Google ScholarGoogle ScholarCross RefCross Ref
  63. Eric Rescorla. 2018. The Transport Layer Security (TLS) Protocol Version 1.3. IETF RFC 8446.Google ScholarGoogle Scholar
  64. Peter Rindal. 2016. libOTe: an efficient, portable, and easy to use Oblivious Transfer Library. https://github.com/osu-crypto/libOTe.Google ScholarGoogle Scholar
  65. Peter Rindal. 2016. libPSI: A Private Set Intersection Library. https://github.com/osu-crypto/libPSI.Google ScholarGoogle Scholar
  66. Peter Rindal and Mike Rosulek. 2017. Improved Private Set Intersection against Malicious Adversaries. In Proceedings of the 36th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT ’17). Springer, 235–259. https://doi.org/10.1007/978-3-319-56620-7_9Google ScholarGoogle ScholarCross RefCross Ref
  67. Peter Rindal and Mike Rosulek. 2017. Malicious-Secure Private Set Intersection via Dual Execution. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ’17). ACM, 1229–1242. https://doi.org/10.1145/3133956.3134044Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Armin Ronacher. 2010. Flask. https://palletsprojects.com/p/flask/.Google ScholarGoogle Scholar
  69. Ahmad-Reza Sadeghi, Christian Wachsmann, and Michael Waidner. 2015. Security and Privacy Challenges in Industrial Internet of Things. In Proceedings of the 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC ’15). ACM. https://doi.org/10.1145/2744769.2747942Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Kamil Salikhov, Gustavo Sacomoto, and Gregory Kucherov. 2014. Using cascading Bloom filters to improve the memory usage for de Brujin graphs. Algorithms for Molecular Biology 9, 1 (2014). https://doi.org/10.1186/1748-7188-9-2Google ScholarGoogle ScholarCross RefCross Ref
  71. Salvatore Sanfilippo. 2009. Redis. https://redis.io/.Google ScholarGoogle Scholar
  72. Christian Schröder. 2016. The Challenges of Industry 4.0 for Small and Medium-sized Enterprises. Technical Report. Friedrich-Ebert-Stiftung.Google ScholarGoogle Scholar
  73. Roholamin Sedighi, Mohammad Saleh Meiabadi, and Mohammadreza Sedighi. 2017. Optimisation of gate location based on weld line in plastic injection moulding using computer-aided engineering, artificial neural network, and genetic algorithm. International Journal of Automotive and Mechanical Engineering 14, 3(2017), 4419–4431. https://doi.org/10.15282/ijame.14.3.2017.3.0350Google ScholarGoogle ScholarCross RefCross Ref
  74. Adi Shamir. 1979. How to Share a Secret. Commun. ACM 22, 11 (1979), 612–613. https://doi.org/10.1145/359168.359176Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. F. Shi, Z. L. Lou, Y. Q. Zhang, and J. G. Lu. 2003. Optimisation of Plastic Injection Moulding Process with Soft Computing. The International Journal of Advanced Manufacturing Technology 21, 9(2003), 656–661. https://doi.org/10.1007/s00170-002-1374-3Google ScholarGoogle ScholarCross RefCross Ref
  76. Ask Solem. 2009. Celery: Distributed Task Queue. http://www.celeryproject.org/.Google ScholarGoogle Scholar
  77. Dawn Xiaoding Song, David Wagner, and Adrian Perrig. 2000. Practical Techniques For Searches On Encrypted Data. In Proceedings of the 2000 IEEE Symposium on Security and Privacy (SP ’00). IEEE, 44–55. https://doi.org/10.1109/SECPRI.2000.848445Google ScholarGoogle ScholarCross RefCross Ref
  78. João Sá Sousa, Cédric Lefebvre, Zhicong Huang, Jean Louis Raisaro, Carlos Aguilar-Melchor, Marc-Olivier Killijian, and Jean-Pierre Hubaux. 2017. Efficient and secure outsourcing of genomic data storage. BMC Medical Genomics 10 (Suppl 2) (2017). https://doi.org/10.1186/s12920-017-0275-0Google ScholarGoogle ScholarCross RefCross Ref
  79. Roberto Spina. 2006. Optimisation of injection moulded parts by using ANN-PSO approach. Journal of Achievements in Materials and Manufacturing Engineering 15, 1–2(2006), 146–152.Google ScholarGoogle Scholar
  80. SQLite. 2000. SQLite. https://www.sqlite.org/.Google ScholarGoogle Scholar
  81. Wencheng Sun, Zhiping Cai, Yangyang Li, Fang Liu, Shengqun Fang, and Guoyan Wang. 2018. Security and Privacy in the Medical Internet of Things: A Review. Security and Communication Networks 2018 (2018). https://doi.org/10.1155/2018/5978636Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. Hasan Tercan, Alexandro Guajardo, Julian Heinisch, Thomas Thiele, Christian Hopmann, and Tobias Meisen. 2018. Transfer-Learning: Bridging the Gap between Real and Simulation Data for Machine Learning in Injection Molding. Procedia CIRP 72(2018), 185–190. https://doi.org/10.1016/j.procir.2018.03.087Google ScholarGoogle ScholarCross RefCross Ref
  83. Kuo-Ming Tsai and Hao-Jhih Luo. 2015. Comparison of injection molding process windows for plastic lens established by artificial neural network and response surface methodology. The International Journal of Advanced Manufacturing Technology 77, 9-12(2015), 1599–1611. https://doi.org/10.1007/s00170-014-6366-6Google ScholarGoogle ScholarCross RefCross Ref
  84. Kuo-Ming Tsai and Hao-Jhih Luo. 2017. An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. Journal of Intelligent Manufacturing 28, 2 (2017), 473–487. https://doi.org/10.1007/s10845-014-0999-zGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  85. VDI Verein Deutscher Ingenieure e.V.2020. VDI – The Association of German Engineers. https://www.vdi.de/en/home.Google ScholarGoogle Scholar
  86. VDMA e. V. (Mechanical Engineering Industry Association). 2015. The VDMA – VDMA. https://www.vdma.org/en/.Google ScholarGoogle Scholar
  87. Karl Weiss, Taghi M. Khoshgoftaar, and DingDing Wang. 2016. A survey of transfer learning. Journal of Big Data 3, 1 (2016), 9. https://doi.org/10.1186/s40537-016-0043-6Google ScholarGoogle ScholarCross RefCross Ref
  88. Katinka Wolter and Philipp Reinecke. 2010. Performance and Security Tradeoff. Proceedings of the 10th International School on Formal Methods for the Design of Computer, Communication and Software Systems (SFM ’10) 6154 (2010), 135–167. https://doi.org/10.1007/978-3-642-13678-8_4Google ScholarGoogle ScholarCross RefCross Ref
  89. Andrew C. Yao. 1982. Protocols For Secure Computations. In Proceedings of the 23rd Annual Symposium on Foundations of Computer Science (SFCS ’82). IEEE, 160–164. https://doi.org/10.1109/SFCS.1982.38Google ScholarGoogle ScholarCross RefCross Ref
  90. Prasad K. D. V. Yarlagadda. 2001. Prediction of processing parameters for injection moulding by using a hybrid neural network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 215, 10 (2001), 1465–1470. https://doi.org/10.1243/0954405011519097Google ScholarGoogle ScholarCross RefCross Ref
  91. zafaco GmbH. 2020. Jahresbericht 2018/19. Technical Report. Breitbandmessung.Google ScholarGoogle Scholar
  92. Junhong Zhang, Jian Wang, Jiewei Lin, Qian Guo, Kongwu Chen, and Liang Ma. 2016. Multiobjective optimization of injection molding process parameters based on Opt LHD, EBFNN, and MOPSO. The International Journal of Advanced Manufacturing Technology 85, 9–12(2016), 2857–2872. https://doi.org/10.1007/s00170-015-8100-4Google ScholarGoogle ScholarCross RefCross Ref
  93. Yuchen Zhang, Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, and Shuang Wang. 2015. FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption. BMC Medical Informatics and Decision Making 15 (Suppl 5) (2015). https://doi.org/10.1186/1472-6947-15-S5-S3Google ScholarGoogle ScholarCross RefCross Ref
  94. Xu Zheng and Zhipeng Cai. 2020. Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs. IEEE Journal on Selected Areas in Communications 38, 5(2020), 968–979. https://doi.org/10.1109/JSAC.2020.2980802Google ScholarGoogle ScholarCross RefCross Ref
  95. Jan Henrik Ziegeldorf, Jan Pennekamp, David Hellmanns, Felix Schwinger, Ike Kunze, Martin Henze, Jens Hiller, Roman Matzutt, and Klaus Wehrle. 2017. BLOOM: BLoom filter based Oblivious Outsourced Matchings. BMC Medical Genomics 10 (Suppl 2) (2017). https://doi.org/10.1186/s12920-017-0277-yGoogle ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Privacy-Preserving Production Process Parameter Exchange
              Index terms have been assigned to the content through auto-classification.

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Other conferences
                ACSAC '20: Proceedings of the 36th Annual Computer Security Applications Conference
                December 2020
                962 pages
                ISBN:9781450388580
                DOI:10.1145/3427228

                Copyright © 2020 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 8 December 2020

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • research-article
                • Research
                • Refereed limited

                Acceptance Rates

                Overall Acceptance Rate104of497submissions,21%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader

              HTML Format

              View this article in HTML Format .

              View HTML Format