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SecureMCMR: Computation Outsourcing for MapReduce Applications

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Cyber Security Cryptography and Machine Learning (CSCML 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12161))

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Abstract

In the last decade, cloud infrastructures such as Google Cloud and Amazon AWS have grown vastly in scale and utilization. Therefore, research into the security and confidentiality of sensitive data passed through these infrastructures is of great importance. We present SecureMCMR, a system that utilizes two public clouds for privacy preserving computation outsourcing for MapReduce applications. We also present analysis of 87 MapReduce applications and the operations they use. Our results on three MapReduce applications show overhead of 160%, 254%, and 380% over plaintext execution.

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Notes

  1. 1.

    The protocol assumes that x and y are integers, however, it is trivially adapted to work over fixpoint representation of real numbers as in the Java implementation of Paillier we use.

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Lindsey Kennard & Ana Milanova

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  1. Lindsey Kennard
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  2. Ana Milanova
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Correspondence to Lindsey Kennard .

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Editors and Affiliations

  1. Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel

    Shlomi Dolev

  2. School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA

    Vladimir Kolesnikov

  3. Tata Consultancy Services, Chennai, Tamil Nadu, India

    Sachin Lodha

  4. Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel

    Gera Weiss

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Kennard, L., Milanova, A. (2020). SecureMCMR: Computation Outsourcing for MapReduce Applications. In: Dolev, S., Kolesnikov, V., Lodha, S., Weiss, G. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2020. Lecture Notes in Computer Science(), vol 12161. Springer, Cham. https://doi.org/10.1007/978-3-030-49785-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-49785-9_10

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