k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving serial data publishing by role composition
Proceedings of the VLDB Endowment
ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Preventing equivalence attacks in updated, anonymized data
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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In today's global information society, governments, companies, public and private institutions and even individuals have to cope with growing demands for personal data publication from scientists, statisticians, journalists and many other data consumers. Current researches on privacy-preserving data publishing by sanitization focus on static dataset, which have no updates. In real life however, data sources are dynamic and usually the updates in these datasets are mainly arbitrary. Then, applying any popular static privacy-preserving technique inevitably yields to information disclosure. Among the few works in the literature that relate to the serial data publication, none of them focuses on arbitrary updates, i.e. with any consistent insert/update/delete sequence, and especially in the presence of auxiliary knowledge that tracks updates of individuals. In this communication, we first highlight the invalidation of existing algorithms and present an extension of the m-invariance generalization model coined τ-safety. Then we formally state the problem of privacy-preserving dataset publication of sequential releases in the presence of arbitrary updates and chainability-based background knowledge. We also propose an approximate algorithm, and we show that our approach to τ-safety, not only prevents from any privacy breach but also achieve a high utility of the anonymous releases.