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.
This work was supported by Engineering Research Center of Excellence Program of Korea Ministry of Education, Science and Technology (MEST) / Korea Science and Engineering Foundation (KOSEF), grant number R11-2008-007-03003-0.
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Jung, E., Ahn, S., Hwang, Sw. (2010). k-ARQ: k-Anonymous Ranking Queries. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_32
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DOI: https://doi.org/10.1007/978-3-642-12026-8_32
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