EPiC: Efficient Privacy-Preserving Counting for MapReduce

  • Triet D. Vo-HuuEmail author
  • Erik-Oliver Blass
  • Guevara Noubir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9466)


In the face of an untrusted cloud infrastructure, outsourced data needs to be protected. We present EPiC, a practical protocol for the privacy-preserving evaluation of a fundamental operation on data sets: frequency counting. We show how a general pattern, defined by a Boolean formula, is arithmetized into a multivariate polynomial and used in EPiC. To increase the performance of the system, we introduce a new efficient privacy-preserving encoding with “somewhat homomorphic” properties based on previous work on the Hidden Modular Group assumption. Besides a formal analysis where we prove EPiC’s privacy, we also present implementation and evaluation results. We specifically target Google’s prominent MapReduce paradigm as offered by major cloud providers. Our evaluation performed both locally and in Amazon’s public cloud with up to 1 TB data sets shows only a modest overhead of \(20\,\%\) compared to non-private counting, attesting to EPiC’s efficiency.


Cloud Computing Homomorphic Encryption Cloud User MapReduce Framework Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by NSF grant 1218197.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Triet D. Vo-Huu
    • 1
    Email author
  • Erik-Oliver Blass
    • 2
  • Guevara Noubir
    • 1
  1. 1.Northeastern UniversityBostonUSA
  2. 2.Airbus Group InnovationsMunichGermany

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