Further Insights: Balancing Privacy, Explainability, and Utility in Machine Learning-based Tabular Data Analysis-Reference-Cited by-同舟云学术

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Further Insights: Balancing Privacy, Explainability, and Utility in Machine Learning-based Tabular Data Analysis

Published:2024-07-30 Issue: Volume:90 Page:1-10
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
language:
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Author:

Abbasi Wisam¹^ORCID,Mori Paolo²^ORCID,Saracino Andrea³^ORCID

Affiliation:

1. Istituto di Informatica e Telematica (IIT), Informatics and Telematics Institute (IIT) of National Research Council, Italy

2. Informatics and Telematics Institute (IIT) of National Research Council, Italy

3. Department of Excellence in Robotics and AI, Scuola Superiore Universitaria Sant'Anna, Italy

Funder

Horizon 2020 Framework Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3670901

Reference38 articles.

1. Deep Learning with Differential Privacy

2. The Explainability-Privacy-Utility Trade-Off for Machine Learning-Based Tabular Data Analysis

3. Visualizing the effects of predictor variables in black box supervised learning models

4. Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In International conference on machine learning. PMLR, 214–223.

5. Moritz Becker. 2018. Understanding users’ health information privacy concerns for health wearables. (2018).

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