nach oben

Neural Computing and Applications

Erschienen in:

01.02.2011 | Original Article

Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

verfasst von: Ankur Bansal, Tingting Chen, Sheng Zhong

Erschienen in: Neural Computing and Applications | Ausgabe 1/2011

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Neural networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties, which is quite common nowadays. Existing cryptographic approaches such as secure scalar product protocol provide a secure way for neural network learning when the training dataset is vertically partitioned. In this paper, we present a privacy preserving algorithm for the neural network learning when the dataset is arbitrarily partitioned between the two parties. We show that our algorithm is very secure and leaks no knowledge (except the final weights learned by both parties) about other party’s data. We demonstrate the efficiency of our algorithm by experiments on real world data.

Vorheriger Artikel A novel Hash algorithm construction based on chaotic neural network

Nächster Artikel Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks

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

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

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+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

Object corresponds to a row in database in this paper

Agrawal D, Srikant R (2000) Privacy preserving data mining. In Proceedings of the ACM SIGMOD, pp 439–450

Blake CL, Merz CJ (1998) UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA. http://www.ics.uci.edu/mlearn/MLRepository.html

Barni M, Orlandi C, Piva A (2006) A privacy-preserving protocol for neural-network-based computation. In: Proceeding of the 8th workshop on multimedia and security, pp 146–151

Cranor LF, Reagle J, Ackerman MS (1999) Beyond concern: understanding net users attitudes about online privacy. Technical report TR 99.4.3, AT&T Labs-Research. Available from http://www.research.att.com/library/trs/TRs/99/99.4/99.4.3/report.htm

Chen T, Zhong S (2009) Privacy preserving back-propagation neural network learning. IEEE Trans Neural Netw 20(10):1554–1564CrossRef

ElGamal T (1985) A public-key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans Inf Theory 31(4):469–472MATHCrossRefMathSciNet

Cranor LF (ed) (1999) Special issue. Internet Priv Commun ACM 42(2)

(2001) Standard for privacy of individually identifiable health information. Fed Regist 66(40). http://www.hhs.gov/ocr/hipaa/finalmaster.html.

Goldreich O, Micali S, Wigderson A (1987) How to play ANY mental game. In: Proceedings of annual ACM conference on theory of computing, pp 218–229

10.

HIPPA, National Standards to Protect the Privacy of Personal Health Information. http://www.hhs.gov/ocr/hipaa/finalreg.html

11.

Jagannathan G, Wright RN (2005) Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the ACM SIGKDD, pp 593–599

12.

Kantarcioglu M, Clifton C (2004) Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans Knowl Data Eng 16(9):1026–1037

13.

Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Proceedings of the 20th annual international cryptology conference on advances in cryptology, pp 36–54

14.

Vaidya J, Clifton C (2002) Privacy preserving association rule mining in vertically partitioned data. In: The eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp 639–644

15.

Wan L, Ng WK, Han S, Lee VCS (2007) Privacy-preservation for gradient descent methods. In: Proceedings of ACM SIGKDD, pp 775–783

16.

Wright R, Yang Z (2004) Privacy preserving Bayesian network structure computation on distributed heterogeneous data. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 713–718

17.

Yao AC (1982) Protocols for secure computations. In: Proceedings of the 23rd annual symposium on foundations of computer science, pp 160–164

18.

Yang Z, Zhong S, Wright R (2005) Privacy-preserving classification of customer data without loss of accuracy. In: Proceedings of the 5th SIAM international conference on data mining (SDM)

Titel: Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
verfasst von: Ankur Bansal
Tingting Chen
Sheng Zhong
Publikationsdatum: 01.02.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0346-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 1/2011

Flexible array self-organizing map neural network and its application in complex pattern recognition

The stochastic approximation method for adaptive Bayesian classifiers: towards online brain–computer interfaces

Bounded robust control of nonlinear systems using neural network–based HJB solution

A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

Fuzzy roughness of n-ary hypergroups based on a complete residuated lattice

Adaptive huberized support vector machine and its application to microarray classification