Security and privacy aspects in MapReduce on clouds: A survey

Philip Derbeko, Shlomi Dolev, Ehud Gudes, Shantanu Sharma

Research output: Contribution to journalReview articlepeer-review

73 Scopus citations


MapReduce is a programming system for distributed processing of large-scale data in an efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is extensively used daily around the world as an efficient distributed computation tool for a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and analysis of social networks. Security and privacy of data and MapReduce computations are essential concerns when a MapReduce computation is executed in public or hybrid clouds. In order to execute a MapReduce job in public and hybrid clouds, authentication of mappers–reducers, confidentiality of data-computations, integrity of data-computations, and correctness–freshness of the outputs are required. Satisfying these requirements shields the operation from several types of attacks on data and MapReduce computations. In this paper, we investigate and discuss security and privacy challenges and requirements, considering a variety of adversarial capabilities, and characteristics in the scope of MapReduce. We also provide a review of existing security and privacy protocols for MapReduce and discuss their overhead issues.

Original languageEnglish (US)
Pages (from-to)1-28
Number of pages28
JournalComputer Science Review
StatePublished - May 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Cloud computing
  • Distributed computing
  • HDFS
  • Hadoop
  • Hybrid cloud
  • MapReduce algorithms
  • Privacy
  • Private cloud
  • Public cloud
  • Security


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