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PRISM – Privacy-Preserving Search in MapReduce

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Part of the Lecture Notes in Computer Science book series (LNSC,volume 7384)


We present PRISM, a privacy-preserving scheme for word search in cloud computing. In the face of a curious cloud provider, the main challenge is to design a scheme that achieves privacy while preserving the efficiency of cloud computing. Solutions from related research, like encrypted keyword search or Private Information Retrieval (PIR), fall short of meeting real-world cloud requirements and are impractical. PRISM ’s idea is to transform the problem of word search into a set of parallel instances of PIR on small datasets. Each PIR instance on a small dataset is efficiently solved by a node in the cloud during the “Map” phase of MapReduce. Outcomes of map computations are then aggregated during the “Reduce” phase. Due to the linearity of PRISM, the simple aggregation of map results yields the final output of the word search operation. We have implemented PRISM on Hadoop MapReduce and evaluated its efficiency using real-world DNS logs. PRISM’s overhead over non-private search is only 11%. Thus, PRISM offers privacy-preserving search that meets cloud computing efficiency requirements. Moreover, PRISM is compatible with standard MapReduce, not requiring any change to the interface or infrastructure.


  • Cloud Computing
  • Internet Service Provider
  • Slave Node
  • Candidate Position
  • Word Search

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.

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  • DOI: 10.1007/978-3-642-31680-7_10
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Blass, EO., Di Pietro, R., Molva, R., Önen, M. (2012). PRISM – Privacy-Preserving Search in MapReduce. In: Fischer-Hübner, S., Wright, M. (eds) Privacy Enhancing Technologies. PETS 2012. Lecture Notes in Computer Science, vol 7384. Springer, Berlin, Heidelberg.

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  • Print ISBN: 978-3-642-31679-1

  • Online ISBN: 978-3-642-31680-7

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