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Efficient discovery of de-identification policy options through a risk-utility frontier.

, , , , and . CODASPY, page 59-70. ACM, (2013)

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Learning Markov Blankets From Multiple Interventional Data Sets., , and . IEEE Trans. Neural Networks Learn. Syst., 31 (6): 2005-2019 (2020)A relative privacy model for effective privacy preservation in transactional data., , , , and . Concurr. Comput. Pract. Exp., (2019)Multi-Label Feature Selection Via Adaptive Label Correlation Estimation., , , , , , and . ACM Trans. Knowl. Discov. Data, 17 (9): 134:1-134:28 (November 2023)A hybrid approach to prevent composition attacks for independent data releases., , , , , and . Inf. Sci., (2016)Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision., , , , , , , , , and . CDPD, volume 218 of Proceedings of Machine Learning Research, page 1-2. PMLR, (2023)Recommending Personalized Interventions to Increase Employability of Disabled Jobseekers., , , , , , and . PAKDD (3), volume 13282 of Lecture Notes in Computer Science, page 92-104. Springer, (2022)Causal Disentanglement for Adversarial Defense., , , and . AI (1), volume 14471 of Lecture Notes in Computer Science, page 315-327. Springer, (2023)Efficient discovery of de-identification policy options through a risk-utility frontier., , , , and . CODASPY, page 59-70. ACM, (2013)Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data., , , and . Health Inf. Sci. Syst., 5 (1): 5 (2017)Causal Feature Selection With Dual Correction., , , , and . IEEE Trans. Neural Networks Learn. Syst., 35 (1): 938-951 (January 2024)