From c71f8f6e2ffbb2bd9d29018b5eb84d9affa90eff Mon Sep 17 00:00:00 2001 From: Maya Anderson Date: Sun, 19 Mar 2023 15:23:38 +0200 Subject: [PATCH] Fix references in README Signed-off-by: Maya Anderson --- apt/risk/data_assessment/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/apt/risk/data_assessment/README.md b/apt/risk/data_assessment/README.md index 03e4887..7e30c06 100644 --- a/apt/risk/data_assessment/README.md +++ b/apt/risk/data_assessment/README.md @@ -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)