Abstract
Differential privacy has recently emerged as one of the strongest privacy guarantees by making few assumptions on the background or external knowledge of an attacker. Differentially private data analysis and publishing have received considerable attention in biomedical communities as promising approaches for sharing medical and health data, while preserving the privacy of individuals represented in data records. In this chapter, we provide a broad survey of the recent works in differentially private histogram and synthetic data publishing. We categorize most recent and emerging techniques in this field from two major aspects: (a) various data types (e.g, relational data, transaction data, dynamic stream data, etc.), and (b) parametric, and non-parametric techniques. We also present some challenges and future research directions for releasing differentially private histogram and synthetic data in health and medical data.
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- 1.
| D | means the data cardinality or the number of tuples in database D.
- 2.
Data cube and histogram are the same objects in this chapter.
- 3.
In linguistics, a copula is a word used to link the subject of a sentence with a predicate, such as the word “is” in the sentence “The sky is blue”. In probability and statistics theory, a copula is a multivariate probability distribution for which the marginal probability distribution of each variable is uniform.
- 4.
Another definition is the removal or addition of a single data record.
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Li, H., Xiong, L., Jiang, X. (2015). Differentially Private Histogram and Synthetic Data Publication. In: Gkoulalas-Divanis, A., Loukides, G. (eds) Medical Data Privacy Handbook. Springer, Cham. https://doi.org/10.1007/978-3-319-23633-9_3
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DOI: https://doi.org/10.1007/978-3-319-23633-9_3
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