Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
OLAP over uncertain and imprecise data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
On privacy preservation against adversarial data mining
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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
OLAP over imprecise data with domain constraints
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On Unifying Privacy and Uncertain Data Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Modeling and Integrating Background Knowledge in Data Anonymization
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Deriving Private Information from Association Rule Mining Results
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Probabilistic Inference Protection on Anonymized Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
ICDT'05 Proceedings of the 10th international conference on Database Theory
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
MaskIt: privately releasing user context streams for personalized mobile applications
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Differentially private transit data publication: a case study on the montreal transportation system
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially private top-k query over MapReduce
Proceedings of the fourth international workshop on Cloud data management
A general framework for privacy preserving data publishing
Knowledge-Based Systems
Hi-index | 0.00 |
Group based anonymization is the most widely studied approach for privacy-preserving data publishing. Privacy models/definitions using group based anonymization includes k-anonymity, l-diversity, and t-closeness, to name a few. The goal of this article is to raise a fundamental issue regarding the privacy exposure of the approaches using group based anonymization. This has been overlooked in the past. The group based anonymization approach by bucketization basically hides each individual record behind a group to preserve data privacy. If not properly anonymized, patterns can actually be derived from the published data and be used by an adversary to breach individual privacy. For example, from the medical records released, if patterns such as that people from certain countries rarely suffer from some disease can be derived, then the information can be used to imply linkage of other people in an anonymized group with this disease with higher likelihood. We call the derived patterns from the published data the foreground knowledge. This is in contrast to the background knowledge that the adversary may obtain from other channels, as studied in some previous work. Finally, our experimental results show such an attack is realistic in the privacy benchmark dataset under the traditional group based anonymization approach.