k-ARQ: k-Anonymous Ranking Queries

  • Eunjin Jung
  • Sukhyun Ahn
  • Seung-won Hwang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5981)

Abstract

With the advent of an unprecedented magnitude of data, top-k queries have gained a lot of attention. However, existing work to date has focused on optimizing efficiency without looking closely at privacy preservation. In this paper, we study how existing approaches have failed to support a combination of accuracy and privacy requirements and we propose a new data publishing framework that supports both areas. We show that satisfying both requirements is an essential problem and propose two comprehensive algorithms. We also validated the correctness and efficiency of our approach using experiments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eunjin Jung
    • 1
  • Sukhyun Ahn
    • 2
  • Seung-won Hwang
    • 2
  1. 1.Dept. of Computer ScienceThe University of Iowa 
  2. 2.Dept. of Computer Science and EngineeringPohang University of Science and Technology (POSTECH) 

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