Privacy-preserving outsourced classification in cloud computing View Full Text


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Article Info

DATE

2018-03

AUTHORS

Ping Li, Jin Li, Zhengan Huang, Chong-Zhi Gao, Wen-Bin Chen, Kai Chen

ABSTRACT

Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys, which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model More... »

PAGES

1-10

References to SciGraph publications

  • 2016-12. Privacy-preserving kernel k-means clustering outsourcing with random transformation in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2005. Evaluating 2-DNF Formulas on Ciphertexts in THEORY OF CRYPTOGRAPHY
  • 2009. Privacy-Preserving Face Recognition in PRIVACY ENHANCING TECHNOLOGIES
  • 2010. Fully Homomorphic Encryption over the Integers in ADVANCES IN CRYPTOLOGY – EUROCRYPT 2010
  • 2013. ML Confidential: Machine Learning on Encrypted Data in INFORMATION SECURITY AND CRYPTOLOGY – ICISC 2012
  • 2008-07. Privacy-preserving Naïve Bayes classification in THE VLDB JOURNAL
  • 2016. Taxonomy of Recent DDoS Attack Prevention, Detection, and Response Schemes in Cloud Environment in PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON RECENT COGNIZANCE IN WIRELESS COMMUNICATION & IMAGE PROCESSING
  • 2011-02. Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data in NEURAL COMPUTING AND APPLICATIONS
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    http://scigraph.springernature.com/pub.10.1007/s10586-017-0849-9

    DOI

    http://dx.doi.org/10.1007/s10586-017-0849-9

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