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Fix references in README

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Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
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Maya Anderson 2023-03-19 15:23:38 +02:00
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apt/risk/data_assessment/README.md
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@ -90,16 +90,16 @@ Citations
---------
[^1]: "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" by D. Chen, N. Yu, Y. Zhang,
M. Fritz published in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 343–62,
2020. [https://doi.org/10.1145/3372297.3417238](https://doi.org/10.1145/3372297.3417238)
M. Fritz in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 343–62, 2020.
[https://doi.org/10.1145/3372297.3417238](https://doi.org/10.1145/3372297.3417238)
[^2]: Code for the paper "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models"
[https://github.com/DingfanChen/GAN-Leaks](https://github.com/DingfanChen/GAN-Leaks)
[https://github.com/DingfanChen/GAN-Leaks](https://github.com/DingfanChen/GAN-Leaks)
[^3]: "Data Synthesis based on Generative Adversarial Networks." by N. Park, M. Mohammadi, K. Gorde, S. Jajodia,
H. Park, and Y. Kim in International Conference on Very Large Data Bases (VLDB), 2018.
H. Park, and Y. Kim in International Conference on Very Large Data Bases (VLDB), 2018.
[^4]: "Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data" by M. Platzer and T. Reutterer.
[^5]: Code for the paper "Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data"
[https://github.com/mostly-ai/paper-fidelity-accuracy](https://github.com/mostly-ai/paper-fidelity-accuracy)
[https://github.com/mostly-ai/paper-fidelity-accuracy](https://github.com/mostly-ai/paper-fidelity-accuracy)