Aggregate query answering on anonymized tables

Qing Zhang, Nick Koudas, Divesh Srivastava, Ting Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

227 Citations (Scopus)


Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and ℓ-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.

Original languageEnglish
Title of host publication23rd International Conference on Data Engineering, ICDE 2007
Number of pages10
Publication statusPublished - 24 Sep 2007
Event23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
Duration: 15 Apr 200720 Apr 2007

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other23rd International Conference on Data Engineering, ICDE 2007


ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Zhang, Q., Koudas, N., Srivastava, D., & Yu, T. (2007). Aggregate query answering on anonymized tables. In 23rd International Conference on Data Engineering, ICDE 2007 (pp. 116-125). [4221660] (Proceedings - International Conference on Data Engineering).