17-04-2012, 03:38 PM
Fast Data Anonymizationwith Low Information Loss
project 2.ppt (Size: 1.3 MB / Downloads: 31)
Privacy-Preserving Data Publishing
Large amounts of public data
Research or statistical purposes
e.g. distribution of disease for age, city
Data may contain sensitive information
Ensure data privacy
Contributions
1D QID
Linear, optimal k-anonymous partitioning
Polynomial, optimal ℓ-diverse partitioning
Linear heuristic for ℓ-diverse partitioning
Generalization to multi-dimensional QID
Multi-to-1D mapping
Hilbert Space-Filling Curve
i-Distance
Apply 1D algorithms
Conclusions
Framework for k-anonymity and ℓ-diversity
Transform the multi-D QID problem to 1-D
Apply linear optimal/heuristic 1D algorithms
Results
Clearly superior utility to Mondrian, with comparable execution time
Similar (or better) utility as Anatomy for aggregate queries, where Anatomy excels