The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
A Generic Approach to Bulk Loading Multidimensional Index Structures
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
The Buffer Tree: A New Technique for Optimal I/O-Algorithms (Extended Abstract)
WADS '95 Proceedings of the 4th International Workshop on Algorithms and Data Structures
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Priority R-tree: a practically efficient and worst-case optimal R-tree
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(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
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Dynamic anonymization: accurate statistical analysis with privacy preservation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A framework for efficient data anonymization under privacy and accuracy constraints
ACM Transactions on Database Systems (TODS)
Data management challenges for computational transportation
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
A reciprocal framework for spatial K-anonymity
Information Systems
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Algorithm-safe privacy-preserving data publishing
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-aware location data publishing
ACM Transactions on Database Systems (TODS)
Non-homogeneous generalization in privacy preserving data publishing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
ACM Transactions on Database Systems (TODS)
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
| Hi-index | 0.00 |
In this paper we observe that k-anonymizing a data set is strikingly similar to building a spatial index over the data set, so similar in fact that classical spatial indexing techniques can be used to anonymize data sets. We use this observation to leverage over 20 years of work on database indexing to provide efficient and dynamic anonymization techniques. Experiments with our implementation show that the R-tree index-based approach yields a batch anonymization algorithm that is orders of magnitude more efficient than previously proposed algorithms and has the advantage of supporting incremental updates. Finally, we show that the anonymizations generated by the R-tree approach do not sacrifice quality in their search for efficiency; in fact, by several previously proposed quality metrics, the compact partitioning properties of R-trees generate anonymizations superior to those generated by previously proposed anonymization algorithms.