Private Polynomial Computation for Noncolluding Coded Databases

Sarah A. Obead, Hsuan Yin Lin, Eirik Rosnes, Jorg Kliewer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We consider private polynomial computation (PPC) over noncolluding coded databases. In such a setting a user wishes to compute a multivariate polynomial of degree at most g over f variables (or messages) stored in multiple databases while revealing no information about the desired polynomial to the databases. We construct two novel PPC schemes, where the first is a generalization of our previous work in private linear computation for coded databases. In this scheme we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product private information retrieval and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates compared to the best known schemes from the literature for a small number of messages, while in the asymptotic case the rates match.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538692912
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: Jul 7 2019Jul 12 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2019 IEEE International Symposium on Information Theory, ISIT 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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