k-ARQ: k-anonymous ranking queries

Authors:
Eunjin Jung;Sukhyun Ahn;Seung-won Hwang
Affiliations:
Dept. of Computer Science, The University of Iowa;Dept. of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH);Dept. of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH)
Venue:
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Year:
2010

Citing 10
Cited 0

Optimal aggregation algorithms for middleware

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On Rectangular Partitionings in Two Dimensions: Algorithms, Complexity, and Applications

ICDT '99 Proceedings of the 7th International Conference on Database Theory
Optimizing Multi-Feature Queries for Image Databases

VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Optimizing Access Cost for Top-k Queries over Web Sources: A Unified Cost-Based Approach

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Mondrian Multidimensional K-Anonymity

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering

Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
L-diversity: Privacy beyond k-anonymity

ACM Transactions on Knowledge Discovery from Data (TKDD)
M-invariance: towards privacy preserving re-publication of dynamic datasets

Proceedings of the 2007 ACM SIGMOD international conference on Management of data

Quantified Score

Hi-index	0.00

Visualization

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.