Skip to main content

2018 | OriginalPaper | Buchkapitel

Privacy Preservation Using Various Anonymity Models

verfasst von : Deepak Narula, Pardeep Kumar, Shuchita Upadhyaya

Erschienen in: Cyber Security

Verlag: Springer Singapore

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

search-config
loading …

Abstract

Need of collection and sharing of data is increasing day by day as it is the requirement of today’s society. While publishing data, one has to guarantee that sensitive information should be made secret so that no one is able to misuse it. For this purpose, one can use various methods and techniques of anonymization. A number of recent researchers are focusing on proposing different anonymity algorithms and techniques to keep published data secret. In this paper, a review of various methods of anonymity with different anonymity operators and various types of linkage attacks has been done. An analysis of the performance of various anonymity algorithms on the basis of various parameters on different data sets using ARX data anonymity software has been done in the end.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Yang X, Ma T, Tang M, Tian W (2014) A survey of privacy preserving data publishing using generalization and suppression. An Int J Appl Math Inf Sci 8(3):1103–1116CrossRef Yang X, Ma T, Tang M, Tian W (2014) A survey of privacy preserving data publishing using generalization and suppression. An Int J Appl Math Inf Sci 8(3):1103–1116CrossRef
2.
Zurück zum Zitat Byun J-W, Kamra A, Li N (2007) Effiecient k-anonymization using clutering techniues, DASFAA 2007, LNCS 4443. Springer, Berlin, pp 188–200 Byun J-W, Kamra A, Li N (2007) Effiecient k-anonymization using clutering techniues, DASFAA 2007, LNCS 4443. Springer, Berlin, pp 188–200
3.
Zurück zum Zitat LevFevre K, Dewitt DJ, Raghu R (2005) Incognito: efficient full-domain k-anonymity. In Proceeding of ACM SIGMOD, pp 49–60, New York, 2005 LevFevre K, Dewitt DJ, Raghu R (2005) Incognito: efficient full-domain k-anonymity. In Proceeding of ACM SIGMOD, pp 49–60, New York, 2005
4.
Zurück zum Zitat Bayardo RJ (2005) Data privacy through optimal k-anonymization. In: International conference on data engineering, pp 217–228, Washington, DC, USA, 2005 Bayardo RJ (2005) Data privacy through optimal k-anonymization. In: International conference on data engineering, pp 217–228, Washington, DC, USA, 2005
5.
Zurück zum Zitat Fung, BCM, Wang K, Yu PS (2005) Top–down specification for information and privacy preservation, In: Proceeding of 21th IEEE international conference on data engineering, ICDE’05, pp 205–216, Tokyo, Japan 2005 Fung, BCM, Wang K, Yu PS (2005) Top–down specification for information and privacy preservation, In: Proceeding of 21th IEEE international conference on data engineering, ICDE’05, pp 205–216, Tokyo, Japan 2005
6.
Zurück zum Zitat Wong RCW, Li J, Fu AWC, Wang K (2006) (α, k)-Anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: Proceeding of 12th international conference on knowledge discovery and data mining, pp 754–759, Philadelphia, PA, 2006 Wong RCW, Li J, Fu AWC, Wang K (2006) (α, k)-Anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: Proceeding of 12th international conference on knowledge discovery and data mining, pp 754–759, Philadelphia, PA, 2006
7.
Zurück zum Zitat Xu J, Wang W, Pei J, Wang X, Shi B, Fu AWC (2006) Utility-base anonymization using local recoding. In: Proceedings of 12th international conference on knowledge discovery and data mining, pp 785–790, Philadelphia, PA, USA, 2006 Xu J, Wang W, Pei J, Wang X, Shi B, Fu AWC (2006) Utility-base anonymization using local recoding. In: Proceedings of 12th international conference on knowledge discovery and data mining, pp 785–790, Philadelphia, PA, USA, 2006
8.
Zurück zum Zitat Mirashe MS, Hande KN (2015) Survey on efficient technique for annonymized microdata preservation. Int J Emerg Dev 2(5):97–103, ISSN 2249-6149 Mirashe MS, Hande KN (2015) Survey on efficient technique for annonymized microdata preservation. Int J Emerg Dev 2(5):97–103, ISSN 2249-6149
9.
Zurück zum Zitat Fung BCM, Wang, K, Fu AWC, Yu PS (2011) Introduction to privacy preserving data publishing concepts and techniques. CRC Press, Taylor and Francis Group, New York, p 13, ISBN 978-1-4200-9148-9 Fung BCM, Wang, K, Fu AWC, Yu PS (2011) Introduction to privacy preserving data publishing concepts and techniques. CRC Press, Taylor and Francis Group, New York, p 13, ISBN 978-1-4200-9148-9
10.
Zurück zum Zitat Sweeney L (2002) k-Anonymity: a model for protecting privacy. Int J Uncertan Fuzziness, Knowl-Based Syst 10:557–570MathSciNetCrossRef Sweeney L (2002) k-Anonymity: a model for protecting privacy. Int J Uncertan Fuzziness, Knowl-Based Syst 10:557–570MathSciNetCrossRef
11.
Zurück zum Zitat Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M (2006) l-Diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd IEEE international conference on data engineering (ICDE), Atlanta, GA, 2006 Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M (2006) l-Diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd IEEE international conference on data engineering (ICDE), Atlanta, GA, 2006
12.
Zurück zum Zitat Ashoka K, Poornima B (2014) A survey of latest developments in privacy preserving data publishing. Int J Adv Inf Sci Technol 32(32):1–10, ISSN 319:2682 Ashoka K, Poornima B (2014) A survey of latest developments in privacy preserving data publishing. Int J Adv Inf Sci Technol 32(32):1–10, ISSN 319:2682
13.
Zurück zum Zitat Machanavajjjhala A, Kifer D, Gehrke J, Venkitasaubramaniam M (2007) l-Diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1(1): 1–57 Machanavajjjhala A, Kifer D, Gehrke J, Venkitasaubramaniam M (2007) l-Diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1(1): 1–57
14.
Zurück zum Zitat Li N, Li T (2007) t-Closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of 21st IEEE international conference on data engineering ICDE), Istanbul, Turkey, April 2007 Li N, Li T (2007) t-Closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of 21st IEEE international conference on data engineering ICDE), Istanbul, Turkey, April 2007
Metadaten
Titel
Privacy Preservation Using Various Anonymity Models
verfasst von
Deepak Narula
Pardeep Kumar
Shuchita Upadhyaya
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-8536-9_13