Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards MyNewsletter Firmensuche
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
Sustainability in Automotive

Gesamtlebenszyklus wird immer wichtiger

In der Digitalkonferenz am 5. Dezember 2024 geht es um die wachsende Bedeutung der Kreislaufwirtschaft in der Automobilindustrie und wie nachhaltige Werkstoffe sowie wiederverwendbare Komponenten dazu beitragen können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Comparative Analysis of Privacy Preserving Approaches for Collaborative Data Processing

verfasst von : Urvashi Solanki, Bintu Kadhiwala

Erschienen in: Intelligent Communication Technologies and Virtual Mobile Networks

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data collection by public and private organizations is increasing for extracting hidden knowledge from it that may be used for assisting decision making process. Moreover, availability of high speed internet and sophisticated data mining tools make sharing of this collected data across various organizations possible. As a consequence, these organizations may share and combine their datasets to retrieve the improved result from the combined data using collaborative data processing. Sharing of such data as it is between collaborative organizations may compromise individual’s privacy as the collected data may contain sensitive information about individuals in its original form. To address this challenge, two main categories of privacy preserving approaches viz. the non-cryptography based approach and the cryptography based approach can be utilized. This paper aims to discuss an insight of these approaches and to highlight the parametric comparison.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel VLSI Implementation of Image Encryption Using DNA Cryptography
Nächstes Kapitel Damage Detection and Evaluation in Wireless Sensor Network for Structural Health Monitoring
Literatur
1.
Chen, B.C., Kifer, D., Lefevre, K., Machanavajjhala, A.: Privacy-preserving data publishing. Found. Trends Databases 2(1–2), 1–167 (2009)CrossRef
2.
Ciriani, V., Di Vimercati, S.D.C., Foresti, S., Samarati, P.: k-anonymous data mining: a survey. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining: Models and Algorithms, pp. 105–136. Springer, Boston (2008)CrossRef
3.
4.
Zhan, Z.: Privacy-Preserving Collaborative Data Mining. Ph.D thesis, University of Ottawa, Canada (2006)
5.
Abbas, A., Khan, S.: A review on the state-of-the-art privacy-preserving approaches in e-health clouds. J. Biomed. Health Inf. IEEE 18(4), 1431–1441 (2014)CrossRef
6.
Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz, R., Longhurst, J., Nordholt, E.S., Seri, G., Wolf, P.: Handbook on Statistical Disclosure Control, ESSnet on Statistical Disclosure Control, version 1.0 (2006)
7.
Pedersen, T.B., Saygin, Y. and Savas, E.: Secret Sharing vs. Encryption-Based Techniques for Privacy Preserving Data Mining (2007)
8.
Das, K.: Privacy preserving distributed data mining based on multi-objective optimization and algorithmic game theory. Ph.D thesis, University of Maryland, Baltimore County (2009)
9.
Fung, B., Wang, K., Chen, R., Yu, P.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. ACM 42(4), 1–53 (2010). Article 14CrossRef
10.
Aldeen, Y.A.A.S., Salleh, M., Razzaque, M.A.: A comprehensive review on privacy preserving data mining. SpringerPlus 4(1), 694 (2015)CrossRef
11.
Wang, J., Luo, Y., Zhao, Y., Le, J.: A survey on privacy preserving data mining. In: First International Workshop on Database Technology and Applications, pp. 111–114. IEEE (2009)
12.
Dalenius, T.: Finding a needle in a haystack - or identifying anonymous census record. J. Official Stat. 2(3), 329–336 (1986)
13.
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRef
14.
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), p. 24 (2006)
15.
Aggarwal, C.C., Yu, P.S.: On static and dynamic methods for condensation-based privacy-preserving data mining. In: ACM Transactions on Database Systems (TODS), vol. 33, no. 1 (2008)CrossRef
16.
Chen, K., Liu, L.: Privacy preserving data classification with rotation perturbation. In: Fifth IEEE International Conference on Data Mining (ICDM 2005) (2005)
17.
Mivule, K.: Utilizing noise addition for data privacy, an overview. In: International Conference on Information and Knowledge Engineering, Las Vegas, USA, pp. 65–71 (2012)
18.
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 19th ACM SIGMOD Conference on Management of Data, vol. 29, no. 2, pp. 439–450. ACM (2000)
19.
Aggarwal, C.C., Philip, S.Y.: A survey of randomization methods for privacy-preserving data mining. In: Aggarwal, C.C., Yu, P.S. (eds.) privacy-preserving data mining. Advances in Database Systems, vol. 34, pp. 137–156. Springer, Boston (2008)CrossRef
20.
Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 37–48 (2005)
21.
Xu, L., Jiang, C., Wang, J., Yuan, J., Ren, Y.: Information security in big data: privacy and data mining. IEEE Access 2, 1149–1176 (2014)CrossRef
22.
Yao, A.C.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167. IEEE (1986)
23.
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4(2), 28–34 (2002)CrossRef
24.
Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Priv. Confidentiality 1(1), 59–98 (2009)
25.
Ge, X., Yan, L., Zhu, J., Shi, W.: Privacy-preserving distributed association rule mining based on the secret sharing technique. In: 2nd International Conference on Software Engineering and Data Mining (SEDM), pp. 345–350. IEEE (2010)
26.
27.
Blakley, G.R.: Safeguarding cryptographic keys. In: Proceedings of the National Computer Conference, vol. 48, pp. 313–317 (1979)
28.
Zhan, J., Blosser, G., Yang, C., Singh, L.: Privacy-preserving collaborative social networks. In: International Conference on Intelligence and Security Informatics, pp. 114–125. Springer, Heidelberg (2008)CrossRef
29.
Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Found. Secure Comput. 4(11), 169–180 (1978)MathSciNet
30.
Yang, Z., Zhong, S., Wright, R.N.: Privacy preserving classification of customer data without loss of accuracy. In: Proceedings of the 5th SIAM International Conference on Data Mining, pp. 92–102 (2005)
31.
Yi, X., Paulet, R., Bertino, E.: Homomorphic Encryption and Applications, vol. 3 (2014)MATH
32.
Burnett, L., Barlow-Stewart, K., Proos, A.L., Aizenberg, H.: The “GeneTrustee”: a universal identification system that ensures privacy and confidentiality for human genetic databases. J. Law Med. 10(4), 506–513 (2003)
33.
Cox, L.H.: Suppression methodology and statistical disclosure control. J. Am. Stat. Assoc. 75(370), 377–385 (1980)CrossRef
34.
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 571–588 (2002)MathSciNetCrossRef
35.
Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (Abstract). In: Proceedings of the Seventeenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 98), vol. 98, p. 188 (1998)
36.
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and specialization, Technical report SRI-CSL-98-04, SRI Intl., pp. 101–132 (1998)
37.
Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Third International Conference on Availability, Reliability and Security, pp. 990–993. IEEE (2008)
38.
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 23rd International Conference on Data Engineering (ICDE 2007), pp. 106–115. IEEE (2007)
39.
Yin, Y., Kaku, I., Tang, J., Zhu, J.: Privacy-preserving data mining. Data Mining Concepts, Methods and Applications in Management and Engineering Design, pp. 101–119. Springer, London (2011)
40.
Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 183–199 Springer, Heidelberg (2004)CrossRef
41.
Li, D., He, X., Cao, L., Chen, H.: Permutation anonymization. J. Intell. Inf. Syst. 47(3), 427–445 (2015)CrossRef
Metadaten
Titel
Comparative Analysis of Privacy Preserving Approaches for Collaborative Data Processing
verfasst von
Urvashi Solanki
Bintu Kadhiwala
Copyright-Jahr
2020
Buch
Intelligent Communication Technologies and Virtual Mobile Networks

Print ISBN: 978-3-030-28363-6

Electronic ISBN: 978-3-030-28364-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-28364-3_18