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Volumn 5012 LNAI, Issue , 2008, Pages 136-147

Privacy-preserving linear fisher discriminant analysis

Author keywords

[No Author keywords available]

Indexed keywords

CRYPTOGRAPHY; DATA PRIVACY; DISCRIMINANT ANALYSIS; FACE RECOGNITION; FISHER INFORMATION MATRIX; LEARNING SYSTEMS;

EID: 44649144617     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68125-0_14     Document Type: Conference Paper
Times cited : (17)

References (17)
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  • 3
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    • Florida, April 22-24
    • Du, W., Han, Y., Chen, S.: Privacy-preserving multivariate statistical analysis: Linear regression and classification. In: Proceedings of the 4th SIAM International Conference on Data Mining, Florida, April 22-24, 2004, pp. 222-233 (2004)
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    • Du, W.1    Han, Y.2    Chen, S.3
  • 5
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    • The use of multiple measurements in taxonomic problems
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  • 7
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    • Han, S., Ng, W.K.: Privacy-Preserving Genetic Algorithms for Rule Discovery. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, 4654, pp. 407-417. Springer, Heidelberg (2007)
    • Han, S., Ng, W.K.: Privacy-Preserving Genetic Algorithms for Rule Discovery. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 407-417. Springer, Heidelberg (2007)
  • 8
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    • Han, S., Ng, W.K.: Privacy-preserving self-organizing map. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, 4654, pp. 428-437. Springer, Heidelberg (2007)
    • Han, S., Ng, W.K.: Privacy-preserving self-organizing map. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 428-437. Springer, Heidelberg (2007)
  • 9
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    • Hong, Z.-Q., Yang, J.-Y.: Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition 24(4), 317-324 (1991)
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    • Hong, Z.-Q.1    Yang, J.-Y.2
  • 10
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    • Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
    • Chicago, Illinois, USA, pp
    • Jagannathan, G., Wright, R.N.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the 8th ACM SIGKDD, Chicago, Illinois, USA, pp. 593-599 (2005)
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  • 12
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    • Privacy preserving data mining
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    • Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36-53. Springer, Heidelberg (2000)
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  • 14
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    • Public-key cryptosystems based on composite degree residuosity classes
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.