PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning

• URL: http://arxiv.org/abs/2010.02529v1
• Date: Tue, 6 Oct 2020 07:04:59 GMT
• Title: PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
• Authors: Yuli Zheng, Zhenyu Wu, Ye Yuan, Tianlong Chen, Zhangyang Wang
• Abstract summary: This paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL) PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance. Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.
• Score: 111.19576084222345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Credit risk modeling has permeated our everyday life. Most banks and financial companies use this technique to model their clients' trustworthiness. While machine learning is increasingly used in this field, the resulting large-scale collection of user private information has reinvigorated the privacy debate, considering dozens of data breach incidents every year caused by unauthorized hackers, and (potentially even more) information misuse/abuse by authorized parties. To address those critical concerns, this paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL). PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance, by (iteratively) weighing between a privacy-risk loss and a utility-oriented loss. PCAL is compared against off-the-shelf options in terms of both utility and privacy protection. Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.

Related papers

• Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning [1.3604778572442302]
  This study identifies image dataset characteristics that affect the utility and vulnerability of private and non-private Convolutional Neural Network (CNN) models. We find that imbalanced datasets increase vulnerability in minority classes, but DP mitigates this issue.
  arXiv  Detail & Related papers  (2024-09-02T15:30:27Z)
• State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey [0.0]
  This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors. It focuses on the emerging field of Privacy-preserving Machine Learning (PPML) As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns.
  arXiv  Detail & Related papers  (2024-02-25T17:31:06Z)
• Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging [52.578054703818125]
  Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive. Differential Privacy (DP) aims to circumvent these susceptibilities by setting a quantifiable privacy budget. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible.
  arXiv  Detail & Related papers  (2023-12-05T12:21:30Z)
• Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework [6.828884629694705]
  This article proposes the conceptual model called PrivChatGPT, a privacy-generative model for LLMs. PrivChatGPT consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data.
  arXiv  Detail & Related papers  (2023-10-19T06:55:13Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Differential Privacy for Credit Risk Model [0.0]
  We assess differential privacy as a solution to address privacy problems. We evaluate one such tool from LeapYear as applied to the Credit Risk modeling domain.
  arXiv  Detail & Related papers  (2021-06-24T09:58:49Z)
• ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning [10.190911271176201]
  Machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. There is an immediate need for a tool that can quantify the privacy risk to data from models. We present ML Privacy Meter, a tool that can quantify the privacy risk to data from models through state of the art membership inference attack techniques.
  arXiv  Detail & Related papers  (2020-07-18T06:21:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.