Toward a multilevel secure relational data model
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
The multilevel relational (MLR) data model
ACM Transactions on Information and System Security (TISSEC)
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
Extending query rewriting techniques for fine-grained access control
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Purpose based access control of complex data for privacy protection
Proceedings of the tenth ACM symposium on Access control models and technologies
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards a more reasonable generalization cost metric for k-anonymization
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Journal of Computer Security
| Hi-index | 0.00 |
As a privacy-preserving microdata publication model, K-Anonymity has some application limits, such as (1) it cannot satisfy the individual-defined k mechanism requirement, and (2) it is attached with a certain extent potential privacy disclosure risk on published microdata, i.e. existing high-probability inference violations under some prior knowledge on k-anonymized microdata that can surely result in personal private information disclosure. We propose the (k, ℓ)-anonymity model with data generalization approach to support more flexible and anti-inference k-anonymization on a tabular microdata, where k indicates the anonymization level of an identifying attribute cluster and ℓ refers to the diversity level of a sensitive attribute cluster on a record. Within the model, k and ℓ are designed on each record and they can be defined subjectively by the corresponding individual. Beside, the model can prevent two kinds of inference attacks for microdata publication, (1) inferring identifying attributes values when their value domains are known; (2) inferring sensitive attributes values with respect to some value associations in the microdata. Further, we propose an algorithm to describe the k-anonymization process in the model. Finally, we take a scenario to illustrate its feasibility, flexibility, and generality.