A novel publicly delegable secure outsourcing algorithm for large-scale matrix multiplication

Malay Kumar, Vaibhav Mishra, Anurag Shukla, Munendra Singh, Manu Vardhan

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Computation of complex mathematical problems are always a challenge of resource constrained clients. A client can outsource the computations to resource abundant cloud server for execution. But this arrangement brings many security and privacy challenges. In this paper, we have presented a secure and efficient algorithm for general computation and scientific problem i.e. matrix multiplication. The proposed algorithm is inspired by the existing algorithm, but we believe that it is imperative to improve the algorithm to enable secure outsourcing of computation. The previous state-of-the art algorithm for matrix multiplication is vulnerable to the Cipher-Text Only Attack (COA) along with Chosen Cipher-Text Attack (CCA) and Known Plain-Text Attack (KPA) and reveal information about the client's data. Hence fails the security requirements of the outsourcing algorithm. The proposed work retains the efficiency benefit of state-of-the-art algorithm, additionally defended the client data against (COA) along with (CCA) and Known Plain-Text Attack (KPA).

Original languageEnglish
Pages (from-to)6445-6455
Number of pages11
JournalJournal of Intelligent and Fuzzy Systems
Volume38
Issue number5
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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