k-APPRP:a Partitioning Based Privacy Preserving k-anonymous Algorithm for Re-publication of Incremental Datasets
Sun Zhi-hui · Journal of Chinese Computer Systems · 2009
Most of the previous works on k-anonymization focused on one-time release of data.However,data is often released continuously to serve various information purposes in reality.The purpose of this study is to develop an effective solution for the re-publication of incremental datasets.By analyzing several possible generalizations in the anonymization for incremental updates,an important monotonic generalization principle is proposed to prevent privacy disclosure in re-publication.Based on the monotonic generalization principle,a partitioning based privacy preserving k-anonymous algorithm k-APPRP for re-publication is proposed.The theoretical analysis and experimental results indicate that k-APPRP can securely anonymize a continuously growing dataset in an efficient manner while assuring high data quality.