Privacy-Preserving Secret Shared Computations Using MapReduce

Shlomi Dolev, Peeyush Gupta, Yin Li, Sharad Mehrotra, Shantanu Sharma

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Data outsourcing allows data owners to keep their data at untrusted clouds that do not ensure the privacy of data and/or computations. One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for trusted private clouds. This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model. Our proposed algorithms prevent an adversary from knowing the database or the query while also preventing output-size and access-pattern attacks. Interestingly, our algorithms do not involve the database owner, which only creates and distributes secret-shares once, in answering any query, and hence, the database owner also cannot learn the query. Logically and experimentally, we evaluate the efficiency of the algorithms on the following parameters: (i) the number of communication rounds (between a user and a server), (ii) the total amount of bit flow (between a user and a server), and (iii) the computational load at the user and the server.

Original languageEnglish (US)
Article number8792131
Pages (from-to)1645-1666
Number of pages22
JournalIEEE Transactions on Dependable and Secure Computing
Issue number4
StatePublished - Jul 1 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


  • Computation and data privacy
  • MapReduce
  • Shamir's secret-sharing
  • data and computation outsourcing
  • distributed computing


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