• DocumentCode
    249376
  • Title

    Lethe: Cluster-Based Indexing for Secure Multi-user Search

  • Author

    Micheli, Eirini C. ; Margaritis, Giorgos ; Anastasiadis, Stergios V.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    323
  • Lastpage
    330
  • Abstract
    Secure keyword search in shared infrastructures prevents stored documents from leaking sensitive information to unauthorized users. A shared index provides confidentiality if it is exclusively used by users authorized to search all the indexed documents. We introduce the Lethe indexing workflow to improve query and update efficiency in secure keyword search. The Lethe workflow clusters together documents with similar sets of authorized users, and creates shared indices for configurable document subsets accessible by the same users. We examine different datasets based on the empirical statistics of a document sharing system and alternative theoretical distributions. We apply Lethe to generate indexing organizations of different tradeoffs between the search and update cost. With measurements over an open-source distributed search engine, we experimentally confirm the improved search and update performance of particular configurations that we introduce.
  • Keywords
    document handling; indexing; query processing; search engines; Lethe indexing; Lethe workflow clusters; cluster-based indexing; document sharing system; open-source distributed search engine; secure multiuser keyword search; sensitive information; shared infrastructures; Indexing; Keyword search; Organizations; Search engines; Silicon; Time factors; access control; confidentiality; data storage; document sharing; outsourced services; privacy; search engines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.54
  • Filename
    6906797