A theoretical basis for perturbation methods
Statistics and Computing
Spatial and non-spatial model-based protection procedures for the release of business microdata
Statistics and Computing
Maximum entropy simulation for microdata protection
Statistics and Computing
A theoretical basis for perturbation methods
Statistics and Computing
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
Privacy preserving data obfuscation for inherently clustered data
International Journal of Information and Computer Security
Random orthogonal matrix masking methodology for microdata release
International Journal of Information and Computer Security
Ordered Data Set Vectorization for Linear Regression on Data Privacy
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Generating Sufficiency-based Non-Synthetic Perturbed Data
Transactions on Data Privacy
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Micro-SOM: A Linear-Time Multivariate Microaggregation Algorithm Based on Self-Organizing Maps
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
Quantile-based bootstrap methods to generate continuous synthetic data
Proceedings of the 2010 EDBT/ICDT Workshops
ONN the use of neural networks for data privacy
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Why swap when you can shuffle? a comparison of the proximity swap and data shuffle for numeric data
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Using mahalanobis distance-based record linkage for disclosure risk assessment
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Information fusion in data privacy: A survey
Information Fusion
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
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The problem of limiting the disclosure of information gathered on a set of companies or individuals (the “respondents”) is considered, the aim being to provide useful information while preserving confidentiality of sensitive information. The paper proposes a method which explicitly preserves certain information contained in the data. The data are assumed to consist of two sets of information on each “respondent”: public data and specific survey data. It is assumed in this paper that both sets of data are liable to be released for a subset of respondents. However, the public data will be altered in some way to preserve confidentiality whereas the specific survey data is to be disclosed without alteration. The paper proposes a model based approach to this problem by utilizing the information contained in the sufficient statistics obtained from fitting a model to the public data by conditioning on the survey data. Deterministic and stochastic variants of the method are considered.