Rough computational methods for information systems
Artificial Intelligence
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
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning cross-level certain and possible rules by rough sets
Expert Systems with Applications: An International Journal
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Publishing Sensitive Transactions for Itemset Utility
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical decision rules mining
Expert Systems with Applications: An International Journal
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
Efficient Multidimensional Suppression for K-Anonymity
IEEE Transactions on Knowledge and Data Engineering
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A vague-rough set approach for uncertain knowledge acquisition
Knowledge-Based Systems
Knowledge and Information Systems
Geometric data perturbation for privacy preserving outsourced data mining
Knowledge and Information Systems
Knowledge and Information Systems
Obtaining scalable and accurate classification in large-scale spatio-temporal domains
Knowledge and Information Systems
Clustering-oriented privacy-preserving data publishing
Knowledge-Based Systems
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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Identity disclosure is one of the most serious privacy concerns in many data mining applications. A well-known privacy model for protecting identity disclosure is k-anonymity. The main goal of anonymizing classification data is to protect individual privacy while maintaining the utility of the data in building classification models. In this paper, we present an approach based on rough sets for measuring the data quality and guiding the process of anonymization operations. First, we make use of the attribute reduction theory of rough sets and introduce the conditional entropy to measure the classification data quality of anonymized datasets. Then, we extend conditional entropy under single-level granulation to hierarchical conditional entropy under multi-level granulation, and study its properties by dynamically coarsening and refining attribute values. Guided by these properties, we develop an efficient search metric and present a novel algorithm for achieving k-anonymity, Hierarchical Conditional Entropy-based Top-Down Refinement (HCE-TDR), which combines rough set theory and attribute value taxonomies. Theoretical analysis and experiments on real world datasets show that our algorithm is efficient and improves data utility.