![[2509.18949] Towards Privacy-Aware Bayesian Networks: A Credal Approach](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2509.18949] Towards Privacy-Aware Bayesian Networks: A Credal Approach
Summary
This paper presents a novel approach to privacy-aware Bayesian networks using credal networks, addressing the trade-off between privacy and model utility in probabilistic graphical models.
Why It Matters
As privacy concerns grow, especially in data-sensitive fields like healthcare and finance, developing models that protect individual data while maintaining utility is crucial. This research introduces credal networks as a promising solution, potentially influencing future privacy-preserving methodologies in machine learning.
Key Takeaways
- Credal networks (CN) can mask learned Bayesian networks (BN) to enhance privacy.
- Balancing privacy and utility is essential for effective probabilistic models.
- The study provides numerical experiments demonstrating the effectiveness of CNs.
- High privacy levels can be achieved without significantly sacrificing model accuracy.
- Key learning information must be concealed to prevent data recovery by attackers.
Computer Science > Machine Learning arXiv:2509.18949 (cs) [Submitted on 23 Sep 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Towards Privacy-Aware Bayesian Networks: A Credal Approach Authors:Niccolò Rocchi, Fabio Stella, Cassio de Campos View a PDF of the paper titled Towards Privacy-Aware Bayesian Networks: A Credal Approach, by Niccol\`o Rocchi and Fabio Stella and Cassio de Campos View PDF HTML (experimental) Abstract:Bayesian networks (BN) are probabilistic graphical models that enable efficient knowledge representation and inference. These have proven effective across diverse domains, including healthcare, bioinformatics and economics. The structure and parameters of a BN can be obtained by domain experts or directly learned from available data. However, as privacy concerns escalate, it becomes increasingly critical for publicly released models to safeguard sensitive information in training data. Typically, released models do not prioritize privacy by design. In particular, tracing attacks from adversaries can combine the released BN with auxiliary data to determine whether specific individuals belong to the data from which the BN was learned. State-of-the-art protection tecniques involve introducing noise into the learned parameters. While this offers robust protection against tracing attacks, it significantly impacts the model's utility, in terms of both the significance and accuracy of the resulting inferences. Hence, high privacy may be attained a...
