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Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack

Journal Article


Abstract


  • © 2018 Elsevier Inc. Naive Bayes (NB) is a simple but highly practical classifier, with a wide range of applications including spam filters, cancer diagnosis and face recognition, to name a few examples only. Consider a situation where a user requests a classification service from a NB classifier server, both the user and the server do not want to reveal their private data to each other. This paper focuses on constructing a privacy-preserving NB classifier that is resistant to an easy-to-perform, but difficult-to-detect attack, which we call the substitution-then-comparison (STC) attack. Without resorting to fully homomorphic encryptions, which has a high computational overhead, we propose a scheme which avoids information leakage under the STC attack. Our key technique involves the use of a “double-blinding” technique, and we show how to combine it with additively homomorphic encryptions and oblivious transfer to hide both parties’ privacy. Furthermore, a completed evaluation shows that the construction is highly practical - most of the computations are in the server's offline phase, and the overhead of online computation and communication is small for both parties.

Authors


  •   Gao, Chong (external author)
  •   Cheng, Qiong (external author)
  •   He, Pei (external author)
  •   Susilo, Willy
  •   Li, Jin (external author)

Publication Date


  • 2018

Citation


  • Gao, C., Cheng, Q., He, P., Susilo, W. & Li, J. (2018). Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences, 444 72-88.

Scopus Eid


  • 2-s2.0-85042844342

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1353

Number Of Pages


  • 16

Start Page


  • 72

End Page


  • 88

Volume


  • 444

Place Of Publication


  • United States

Abstract


  • © 2018 Elsevier Inc. Naive Bayes (NB) is a simple but highly practical classifier, with a wide range of applications including spam filters, cancer diagnosis and face recognition, to name a few examples only. Consider a situation where a user requests a classification service from a NB classifier server, both the user and the server do not want to reveal their private data to each other. This paper focuses on constructing a privacy-preserving NB classifier that is resistant to an easy-to-perform, but difficult-to-detect attack, which we call the substitution-then-comparison (STC) attack. Without resorting to fully homomorphic encryptions, which has a high computational overhead, we propose a scheme which avoids information leakage under the STC attack. Our key technique involves the use of a “double-blinding” technique, and we show how to combine it with additively homomorphic encryptions and oblivious transfer to hide both parties’ privacy. Furthermore, a completed evaluation shows that the construction is highly practical - most of the computations are in the server's offline phase, and the overhead of online computation and communication is small for both parties.

Authors


  •   Gao, Chong (external author)
  •   Cheng, Qiong (external author)
  •   He, Pei (external author)
  •   Susilo, Willy
  •   Li, Jin (external author)

Publication Date


  • 2018

Citation


  • Gao, C., Cheng, Q., He, P., Susilo, W. & Li, J. (2018). Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences, 444 72-88.

Scopus Eid


  • 2-s2.0-85042844342

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1353

Number Of Pages


  • 16

Start Page


  • 72

End Page


  • 88

Volume


  • 444

Place Of Publication


  • United States