On the representation and querying of sets of possible worlds
Selected papers of the workshop on Deductive database theory
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Foundations of probabilistic answers to queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient anonymity-preserving data collection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 16th international conference on World Wide Web
From data privacy to location privacy: models and algorithms
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
On Unifying Privacy and Uncertain Data Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Prediction promotes privacy in dynamic social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Minimizing minimality and maximizing utility: analyzing method-based attacks on anonymized data
Proceedings of the VLDB Endowment
Testing software in age of data privacy: a balancing act
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Differentially private summaries for sparse data
Proceedings of the 15th International Conference on Database Theory
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Data anonymization techniques have been the subject of intense investigation in recent years, for many kinds of structured data, including tabular, graph and item set data. They enable publication of detailed information, which permits ad hoc queries and analyses, while guaranteeing the privacy of sensitive information in the data against a variety of attacks. In this tutorial, we aim to present a unified framework of data anonymization techniques, viewed through the lens of uncertainty. Essentially, anonymized data describes a set of possible worlds, one of which corresponds to the original data. We show that anonymization approaches such as suppression, generalization, perturbation and permutation generate different working models of uncertain data, some of which have been well studied, while others open new directions for research. We demonstrate that the privacy guarantees offered by methods such as k-anonymization and l-diversity can be naturally understood in terms of similarities and differences in the sets of possible worlds that correspond to the anonymized data. We describe how the body of work in query evaluation over uncertain databases can be used for answering ad hoc queries over anonymized data in a principled manner. A key benefit of the unified approach is the identification of a rich set of new problems for both the Data Anonymization and the Uncertain Data communities.