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Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations.

, , , , , , and . IEEE Symposium on Security and Privacy Workshops, page 32-40. IEEE Computer Society, (2015)

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Breaking the Activation Function Bottleneck through Adaptive Parameterization., , , and . NeurIPS, page 7750-7761. (2018)Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata., , and . PSD, volume 13463 of Lecture Notes in Computer Science, page 234-249. Springer, (2022)Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations., , , , , , and . IEEE Symposium on Security and Privacy Workshops, page 32-40. IEEE Computer Society, (2015)A Computational Algorithm for Handling the Special Uniques Problem, , and . International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (5): 493-510 (2002)Understanding the Data Environment., and . XRDS, 20 (1): 36-39 (2013)Differential Correct Attribution Probability for Synthetic Data: An Exploration., , , and . PSD, volume 11126 of Lecture Notes in Computer Science, page 122-137. Springer, (2018)Data Environment Analysis and the Key Variable Mapping System., , , and . Privacy in Statistical Databases, volume 6344 of Lecture Notes in Computer Science, page 138-147. Springer, (2010)A measure of disclosure risk for microdata, and . Journal of the Royal Statistical Society: series B (statistical methodology), 64 (4): 855-867 (2002)Multi-objective evolutionary GAN for tabular data synthesis., , , , and . GECCO, ACM, (2024)A Genetic Algorithm Approach to Synthetic Data Production., , and . PrAISe@ECAI, page 13:1-13:4. ACM, (2016)