Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Datafly: A System for Providing Anonymity in Medical Data
Proceedings of the IFIP TC11 WG11.3 Eleventh International Conference on Database Securty XI: Status and Prospects
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
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A simple and fast algorithm for K-medoids clustering
Expert Systems with Applications: An International Journal
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Privacy protection on sliding window of data streams
COLCOM '07 Proceedings of the 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing
Anonymizing Streaming Data for Privacy Protection
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
CASTLE: A delay-constrained scheme for ks-anonymizing data streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Window specification over data streams
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
| Hi-index | 0.00 |
Streaming data is widely used in today's world. Data comes from different sources in streams, and must be processed online and with minimum delay. These data streams usually contain confidential data such as customers' purchase information, and need to be mined in order to reveal other useful information like customers' purchase patterns. Privacy preservation throughout these processes plays a crucial role. K-anonymity is a well-known technique for preserving privacy. The principle issues in k-anonymity are data loss and running time. Although some of the existing k-anonymity techniques are able to generate anonymized data with acceptable data loss, their main drawback is that they are very time consuming, and are not applicable in a streaming context since streaming data is usually very sensitive to delay, and needs to be processed quite fast. In this paper, we propose a cluster-based k-anonymity algorithm called FAANST (Fast Anonymizing Algorithm for Numerical Streaming daTa) which can anonymize numerical streaming data quite fast, while providing an admissible data loss. We also show that FAANST can be easily extended to support data streams consisting of categorical values as well as numerical values.