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Bistochastic privacy
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- We introduce a new privacy model relying on bistochastic matrices, that is, matrices whose components are nonnegative and sum to 1 both row-wise and column-wise. This class of matrices is used to both define privacy guarantees and a tool to apply protection on a data set. The bistochasticity assumption happens to connect several fields of the privacy literature, including the two most popular models, k-anonymity and differential privacy. Moreover, it establishes a bridge with information theory, which simplifies the thorny issue of evaluating the utility of a protected data set. Bistochastic privacy also clarifies the trade-off between protection and utility by using bits, which can be viewed as a natural currency to comprehend and operationalize this trade-off, in the same way than bits are used in information theory to capture uncertainty. A discussion on the suitable parameterization of bistochastic matrices to achieve the privacy guarantees of this new model is also provided.<br />Comment: To be published in Lecture Notes in Artificial Intelligence vol 13408, Modeling Decisions for Artificial Intelligence 19th International Conference MDAI 2022, Sant Cugat, Catalonia, August 30 - 2 September 2022
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....0738595bc5eb64844fa0c9fec114e7f1
- Full Text :
- https://doi.org/10.48550/arxiv.2207.03940