On Rényi Differential Privacy in Statistics-based Synthetic Data Generation
Author:
Affiliation:
1. NTT Social Informatics Laboratories
2. Faculty of Informatics, Gunma University
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/31/0/31_812/_pdf
Reference51 articles.
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2. [2] Aggarwal, C.C.: On k-anonymity and the curse of dimensionality, VLDB, Vol.5, pp.901-909 (2005).
3. [3] Annamalai, M.S.M.S., Gadotti, A. and Rocher, L.: A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data, arXiv preprint arXiv:2301.10053 (2023).
4. [4] Asghar, H.J., Ding, M., Rakotoarivelo, T., Mrabet, S. and Kaafar, D.: Differentially private release of datasets using Gaussian copula, Journal of Privacy and Confidentiality, Vol.10, No.2 (2020).
5. [5] Bond-Taylor, S., Leach, A., Long, Y. and Willcocks, C.G.: Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models, IEEE Trans. Pattern Analysis and Machine Intelligence (2021).
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