Keep a Few: Outsourcing Data While Maintaining Confidentiality

  • Valentina Ciriani
  • Sabrina De Capitani di Vimercati
  • Sara Foresti
  • Sushil Jajodia
  • Stefano Paraboschi
  • Pierangela Samarati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5789)


We put forward a novel paradigm for preserving privacy in data outsourcing which departs from encryption. The basic idea behind our proposal is to involve the owner in storing a limited portion of the data, and maintaining all data (either at the owner or at external servers) in the clear. We assume a relational context, where the data to be outsourced is contained in a relational table. We then analyze how the relational table can be fragmented, minimizing the load for the data owner. We propose several metrics and present a general framework capturing all of them, with a corresponding algorithm finding a heuristic solution to a family of NP-hard problems.


Weight Function Priority Queue Relation Schema Data Owner Query Execution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Valentina Ciriani
    • 1
  • Sabrina De Capitani di Vimercati
    • 1
  • Sara Foresti
    • 1
  • Sushil Jajodia
    • 2
  • Stefano Paraboschi
    • 3
  • Pierangela Samarati
    • 1
  1. 1.DTIUniversità degli Studi di MilanoCremaItalia
  2. 2.CSISGeorge Mason UniversityFairfaxUSA
  3. 3.DIIMMUniversità degli Studi di BergamoDalmineItalia

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