Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
13th International Engine Congress 2026 | 24 + 25 February 2026

Meeting Place for the Powertrain and Sustainable Fuels Community

Take part as a speaker in the 13th International Engine Congress 2026, the Meeting Place for the Powertrain, and Sustainable Fuels Community for the exchange of experience. Submit a subject proposal online about our main subject areas: Passenger car engine technology, Commercial vehicle engine technology and Sustainable Fuels & Energy!
EXTENDED SEARCH
Log in
Top
Journal of Business Economics
Published in:

05-01-2018 | Original Paper

Privacy-preserving condition-based forecasting using machine learning

Authors: Fabian Taigel, Anselme K. Tueno, Richard Pibernik

Published in: Journal of Business Economics | Issue 5/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

As machines get smarter, massive amounts of condition-based data from distributed sources become available. This data can be used to enhance maintenance management in several ways, such as by improving maintenance demand forecasting and spare parts and capacity planning. Regarding the former, machine learning techniques promise substantial benefits for forecasting the demand for spare parts over conventional techniques that are commonly used. While development and implementation of these techniques is difficult, practical applications pose another important challenge to providers of maintenance, repair, and overhaul services. Their customers are reluctant to provide access to sensitive real-time data because of privacy concerns, and even more so when their data is stored and processed in the cloud. In this paper we describe an application for privacy-preserving forecasting of demand for spare parts based on distributed condition data. It combines machine learning techniques—more specifically, decision-tree classification—with order-preserving encryption. The application is appropriate whenever planning for spare parts for the maintenance of condition-monitored machinery is needed, and it is particularly suitable for cloud-based implementation.
previous article Editorial
next article Big data on the shop-floor: sensor-based decision-support for manual processes

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Journal of Business Economics

From January 2013, the Zeitschrift für Betriebswirtschaft (ZfB) is published in English under the title Journal of Business Economics (JBE). The Journal of Business Economics (JBE) aims at encouraging theoretical and applied research in the field of business economics and business administration, promoting the exchange of ideas between science and practice.

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Footnotes
1
Namely, the EU-funded PRACTICE project. For more information, please visit https://​practice-project.​eu/​.​
 
2
For a more general introduction the reader is referred to (Quinlan 1986; Rokach and Maimon 2008; Murphy 2012; Lomax and Vadera 2013).
 
3
In SMC semi-honest (also called honest-but-curious) parties follow the protocol specification. In our setting this means that APPs won’t willingly corrupt or block the transmission of shares.
 
4
Except for support vector machines, all techniques discussed in Sect. 3 satisfy this requirement.
 
Literature
Agrawal R, Kiernan J, Srikant R, Xu Y (2004) Order preserving encryption for numeric data. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, SIGMOD ’04, pp 563–574
Babaï MZ, Syntetos AA, Dallery Y, Nikolopoulos K (2009) Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation. Int J Prod Res 47(9):2461–2483CrossRef
Barkataki S, Zeineddine H (2015) On achieving secure collaboration in supply chains. Inf Syst Front 17(3):691–705CrossRef
Barratt M (2004) Understanding the meaning of collaboration in the supply chain. Supply Chain Manag Int J 9(1):30–42CrossRef
Beimel A (2011) Secret-sharing schemes: a survey. In: Proceedings of the Third International Conference on Coding and Cryptology, Springer-Verlag, Berlin, Heidelberg, IWCC’11, pp 11–46
Bost R, Popa RA, Tu S, Goldwasser S (2015) Machine learning classification over encrypted data. In: 22nd Annual Network and Distributed System Security Symposium, NDSS 2015, San Diego, California, USA
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, ACM, New York, NY, USA, ICML ’06, pp 161–168
Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pp 96–103
Coraddu A, Oneto L, Ghio A, Savio S, Anguita D, Figari M (2016) Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proc Inst Mech Eng, Part M: J Eng Maritime Environ 230(1):136–153CrossRef
Cramer R, Damgrd IB, Nielsen JB (2015) Secure multiparty computation and secret sharing, 1st edn. Cambridge University Press, New YorkCrossRef
Dahan H, Cohen S, Rokach L, Maimon O (2014) Proactive data mining with decision trees. SpringerBriefs in Electrical and Computer Engineering. Springer, New YorkCrossRef
Dekker R, Pinçe Ç, Zuidwijk R, Jalil MN (2013) On the use of installed base information for spare parts logistics: a review of ideas and industry practice. Int J Prod Econ 143(2):536–545CrossRef
Deloux E, Castanier B, Bérenguer C (2009) Predictive maintenance policy for a gradually deteriorating system subject to stress. Reliability Eng Syst Saf 94(2):418–431CrossRef
Deshpande V, Schwarz LB (2006) Secure collaborative planning, forecasting, and replenishment. CERIAS Tech Report 2006–65:2006
Deshpande V, Iyer AV, Cho R (2006) Efficient supply chain management at the u.s. coast guard using part-age dependent supply replenishment policies. Oper Res 54(6):1028–1040CrossRef
Ducas L, Micciancio D (2015) Fhew: bootstrapping homomorphic encryption in less than a second. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, pp 617–640
Elwany AH, Gebraeel NZ (2008) Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Trans 40(7):629–639CrossRef
Frank E, Hall M (2001) A simple approach to ordinal classification. In: Proceedings of the 12th European Conference on Machine Learning, Springer-Verlag, London, UK, UK, EMCL ’01, pp 145–156
Gentry C (2009) Fully homomorphic encryption using ideal lattices. In: Mitzenmacher M (ed) Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, ACM, New York, NY, USA, STOC ’09, pp 169–178
Gentry C, Halevi S, Smart NP (2012) Homomorphic evaluation of the aes circuit. In: Safavi-Naini R, Canetti R (eds) Advances in Cryptology - CRYPTO 2012, vol 7417. Lecture Notes in Computer Science. Berlin Heidelberg, Berlin, Heidelberg, pp 850–867
Ghobbar A, Friend C (2002) Sources of intermittent demand for aircraft spare parts within airline operations. J Air Transp Manag 8(4):221–231CrossRef
Hellingrath B, Cordes AK (2014) Conceptual approach for integrating condition monitoring information and spare parts forecasting methods. Prod Manuf Res Open Access J 2(1):725–737
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510CrossRef
Jawurek M, Kerschbaum F (2012) Fault-tolerant privacy-preserving statistics. In: Privacy Enhancing Technologies - 12th International Symposium, PETS 2012, Vigo, Spain, July 11-13, 2012. Proceedings, pp 221–238
Kerschbaum F (2009) Adapting privacy-preserving computation to the service provider model. In: Proceedings of the 12th IEEE International Conference on Computational Science and Engineering, CSE 2009, Vancouver, BC, Canada, August 29-31, 2009, pp 34–41
Kurz J (2016) Capacity planning for a maintenance service provider with advanced information. Eur J Oper Res 251(2):466–477CrossRef
Letourneau S, Famili F, Matwin S (1999) Data mining to predict aircraft component replacement. IEEE Intell Syst 14(6):59–66CrossRef
Lin X, Basten R, Kranenburg A, van Houtum GJ (2012) Condition based spare parts supply. Beta Working Paper series (371)
Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Bellare M (ed) Advances in cryptology-CRYPTO 2000, Springer, Berlin and New York, Lecture notes in computer science, vol 1880, pp 36–54
Lomax S, Vadera S (2013) A survey of cost-sensitive decision tree induction algorithms. ACM Comput Surv 45(2):1–35CrossRef
Matt G (2005) Adaptive einflussgröenkombination (aek) - prognosen mit schrittweiser regression und adaptivem gewichten. In: Mertens P, Rässler S (eds) Prognoserechnung. Physica-Verlag, Heidelberg, pp 125–168CrossRef
Mertens P, Zeller AJ, Große-Wilde J (2012) Kooperative vorhersage in unternehmensnetzwerken. In: Mertens, Rässler (Hg.) 2012 – Prognoserechnung, pp 621–637
Murphy KP (2012) Machine learning: a probabilistic perspective. Adaptive computation and machine learning series. MIT Press, Cambridge
Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50(1–4):297–313CrossRef
Popa RA, Redfield CMS, Zeldovich N, Balakrishnan H (2011) Cryptdb: Protecting confidentiality with encrypted query processing. In: Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles, ACM, New York, NY, USA, SOSP ’11, pp 85–100
Popa RA, Li FH, Zeldovich N (2013) An ideal-security protocol for order-preserving encoding. In: Proceedings of the 2013 IEEE Symposium on Security and Privacy, IEEE Computer Society, Washington, DC, USA, SP ’13, pp 463–477
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Rokach L, Maimon O (2008) Data mining with decision trees: theory and applications, Series in machine perception and artificial intelligence, vol 69. World Scientific, Singapore
Schneier B (1995) Applied Cryptography (2Nd Ed.): Protocols, Algorithms, and Source Code in C. John Wiley & Sons, Inc., New York, NY, USA
Seifert D (2006) Konzepte des supply-chain-managements—cpfr als unternehmensübergreifende lösung. In: Zentes J (ed) Handbuch Handel. Gabler, Wiesbaden, pp 781–794CrossRef
van Wingerden E, Basten R, Dekker R, Rustenburg WD (2014) More grip on inventory control through improved forecasting: a comparative study at three companies. Int J Prod Econ 157:220–237CrossRef
Veenstra AW, Zuidwijk R, Geerling B (2006) Maintenance logistics in the dutch dredging industry. In: Service Operations and Logistics, and Informatics, 2006. SOLI’06. IEEE International Conference on, IEEE, pp 436–441
Viswanathan S, Widiarta H, Piplani R (2007) Value of information exchange and synchronization in a multi-tier supply chain. Int J Prod Res 45(21):5057–5074CrossRef
Wu DJ, Feng T, Naehrig M, Lauter KE (2016) Privately evaluating decision trees and random forests. PoPETs 4:335–355
Yam R, Tse P, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17(5):383–391CrossRef
Metadata
Title
Privacy-preserving condition-based forecasting using machine learning
Authors
Fabian Taigel
Anselme K. Tueno
Richard Pibernik
Publication date
05-01-2018
Publisher
Springer Berlin Heidelberg
Published in
Journal of Business Economics / Issue 5/2018
Print ISSN: 0044-2372
Electronic ISSN: 1861-8928
DOI
https://doi.org/10.1007/s11573-017-0889-x

Other articles of this Issue 5/2018

A systematic literature review of mining weak signals and trends for corporate foresight

  • Original Paper

Big data on the shop-floor: sensor-based decision-support for manual processes

  • Original Paper

Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

  • Open Access
  • Original Paper

Editorial

  • Editorial

Premium Partner

    Image Credits