k-anonymity: a model for protecting privacy
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
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
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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data & Knowledge Engineering
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Towards optimal k-anonymization
Data & Knowledge Engineering
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
A framework for efficient data anonymization under privacy and accuracy constraints
ACM Transactions on Database Systems (TODS)
Privacy protection on sliding window of data streams
COLCOM '07 Proceedings of the 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Anonymizing Streaming Data for Privacy Protection
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Non-homogeneous generalization in privacy preserving data publishing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
B-CASTLE: An Efficient Publishing Algorithm for K-Anonymizing Data Streams
GCIS '10 Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems - Volume 02
CASTLE: Continuously Anonymizing Data Streams
IEEE Transactions on Dependable and Secure Computing
CASTLE: Continuously Anonymizing Data Streams
IEEE Transactions on Dependable and Secure Computing
FAANST: fast anonymizing algorithm for numerical streaming data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Weak k-anonymity: a low-distortion model for protecting privacy
ISC'06 Proceedings of the 9th international conference on Information Security
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
Clustering-oriented privacy-preserving data publishing
Knowledge-Based Systems
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Research on the anonymization of static data has made great progress in recent years. Generalization and suppression are two common technologies for quasi-identifiers' anonymization. However, the characteristics of data streams, such as potential infinity and high dynamicity, make the anonymization of data streams different from the anonymization of static data. The methods for static data anonymization cannot be directly applied to anonymizing data streams. In this paper, a novel k-anonymization approach for data streams based on clustering is proposed. In order to speed up the anonymization process and reduce the information loss, the new approach scans a stream in one turn to recognize and reuse the clusters satisfying the k-anonymity principle. The time constraints on tuple publication and cluster reuse, which are specific to data streams, are considered as well. Furthermore, the approach is improved to conform to the @?-diversity principle. The experiments conducted on the real datasets show that the proposed methods are both efficient and effective.