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Secure Computation of Inner Product of Vectors with Distributed Entries and Its Applications to SVM

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Information Security Practice and Experience (ISPEC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11125))

Abstract

Nowadays organizations and individuals outsource computation and storage to cloud. This poses a threat to the privacy of users. Different users encrypt their private data with (possibly) different keys to prevent any kind of outside attack on their privacy. In this outsourced model of computation where the data owners have already encrypted and uploaded private data, to enable the users for collaborative data mining a scheme is needed that can process encrypted data under multiple keys. Privacy preserving inner product computation is an essential tool on which many data mining algorithms are based. Several papers address the problem of outsourced privacy preserving inner product computation but none of them deals with the scenario when the entire database is arbitrarily partitioned among the users. We propose two outsourced privacy preserving protocols for computation of inner product of vectors when the underlying database is arbitrarily partitioned. We provide an SVM training model that preserves the privacy of the user’s data-vectors. Our scheme is based on an integer vector encryption scheme.

S. Dutta—Grateful to the NICT, Japan for granting a financial support under the NICT International Exchange Program.

S. Ruj—This work is partially supported by Cisco University Research Program Fund, CyberGrants ID: 698039 and Silicon Valley Community Foundation.

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Author information

Authors and Affiliations

  1. R. C. Bose Centre for Cryptology and Security, Indian Statistical Institute, Kolkata, India

    Sabyasachi Dutta

  2. Cryptology and Security Research Unit, Computer and Communication Sciences Division, Indian Statistical Institute, Kolkata, India

    Nishant Nikam & Sushmita Ruj

Authors
  1. Sabyasachi Dutta
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  2. Nishant Nikam
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  3. Sushmita Ruj
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Corresponding author

Correspondence to Sabyasachi Dutta .

Editor information

Editors and Affiliations

  1. University of Aizu, Aizuwakamatsu, Japan

    Chunhua Su

  2. Meiji University, Tokyo, Japan

    Hiroaki Kikuchi

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Dutta, S., Nikam, N., Ruj, S. (2018). Secure Computation of Inner Product of Vectors with Distributed Entries and Its Applications to SVM. In: Su, C., Kikuchi, H. (eds) Information Security Practice and Experience. ISPEC 2018. Lecture Notes in Computer Science(), vol 11125. Springer, Cham. https://doi.org/10.1007/978-3-319-99807-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-99807-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99806-0

  • Online ISBN: 978-3-319-99807-7

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