1 / 19

On the Anonymization of Sparse High-Dimensional Data

On the Anonymization of Sparse High-Dimensional Data. 1 National University of Singapore {ghinitag,kalnis}@comp.nus.edu.sg 2 Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk. Publishing Transaction Data. Publishing transaction data Retail chain-owned shopping cart data

Download Presentation

On the Anonymization of Sparse High-Dimensional Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. On the Anonymization of Sparse High-Dimensional Data 1 National University of Singapore {ghinitag,kalnis}@comp.nus.edu.sg 2 Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk

  2. Publishing Transaction Data • Publishing transaction data • Retail chain-owned shopping cart data • Infer consumer spending patterns • Correlations among purchased items • e.g., 90% of cereals buyers also buy milk • What about privacy?

  3. Privacy Threat Quasi-identifying Items Sensitive Items

  4. Privacy Paradigm • ℓ-diversity • prevent association between quasi-identifier and sensitive attributes • Create groups of transactions • freq. of an SA value in a group < 1/p • Objective • Enforce privacy • Preserve correlations among items • Challenge: high data dimensionality

  5. Data Re-organization PRESERVES CORELATIONS! Band Matrix Organization

  6. Published Data Summary of Sensitive Items

  7. Contributions • Novel data representation • Preserves correlation among items • Efficient heuristic for group formation • Linear time to data size • Supports multiple sensitive items

  8. State-of-the-art: Mondrian[FWR06] • Generalization-based • data-space partitioning • similar to k-d-trees • split recursively until privacy condition does not hold • constrained global recoding k = 2 Age 20 40 60 GENERALIZATION + HIGH DIMENSIONALITY = UNACCEPTBLE INFORMATION LOSS 40 60 Weight 80 100 [FWR06] K. LeFevre et al. Mondrian Multidimensional k-anonymity, Proceedings of the 22nd International Conference on Data Engineering (ICDE), 2006

  9. State-of-the-art: Anatomy[XT06] • Permutation-based method • discloses exact QID values “Anatomized” table RANDOM GROUP FORMATION DOES NOT PRESERVE CORRELATIONS |G|! permutations [XT06] X. Xiao and Y. Tao. Anatomy: simple and effective privacy preservation, Proceedings of the 32nd international conference on Very Large Data Bases (VLDB), 2006

  10. Bandwidth = U+L+1 Minimizing bandwidth is NP-hard Band Matrix Representation

  11. Reverse Cuthil-McKee (RCM) • Heuristic Bandwidth Minimization • Solves corresponding graph labeling problem • Permutes rows and columns • Complexity N* D * log D • N = matrix rows (# transactions) • D = maximum degree of any vertex

  12. Group Formation • Correlation-aware Anonymization of High-Dimensional Data (CAHD) • Use the order given by RCM • Consecutive transactions highly correlated • O(pN) complexity

  13. Group Formation

  14. Experimental Evaluation

  15. RCM Visualization

  16. Experimental Setting • BMS dataset • Compare with hybrid PermMondrian(PM) • Combines Mondrian with Anatomy • Query Workload • Reconstruction Error

  17. Recostruction Error vs p

  18. Execution Time

  19. Conclusions • Anonymizing transaction data • High-dimensionality • Preserving correlation • Future work • Different encodings for data representation • Enhance correlation among consecutive rows

More Related