核心概念
The KIPPS framework enhances privacy-preserving synthetic data generation by incorporating domain knowledge from Knowledge Graphs to address challenges related to data diversity, complexity, and domain-specific constraints.
摘要
The rapid evolution of machine learning has led to a large demand for data across diverse domains. However, data sharing is often restricted due to privacy and confidentiality concerns. Synthetic data generation offers a scalable solution to this problem, allowing organizations to collaborate on research and analysis without compromising sensitive information.
The paper introduces the KIPPS framework, which infuses domain knowledge from Knowledge Graphs into Generative Deep Learning models to enhance the generation of realistic and domain-compliant synthetic data. The key aspects of the KIPPS framework are:
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Adding Domain Context to Training Data:
- Replacing specific attribute values with broader domain properties to increase diversity and reduce complexity.
- Grouping attributes by their properties to simplify the model's input.
- Incorporating domain-specific conditional rules to guide the model's understanding of the data.
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Conditional Training with Domain Rule Enforced Loss:
- Representing the tabular data with a combination of continuous, discrete, and rule-based features.
- Enforcing domain rules during the training of the Generative Adversarial Network (GAN) model to ensure the generated data adheres to the constraints.
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Differentially Private Discriminator:
- Incorporating differential privacy techniques, specifically Differential Privacy Stochastic Gradient Descent (DP-SGD), to the discriminator of the GAN model to provide a provable privacy guarantee for the synthetic data.
The KIPPS framework is evaluated on real-world datasets from various domains, including cybersecurity, healthcare, and socio-economic studies. The results demonstrate that KIPPS effectively balances high similarity and utility metrics with strong privacy protection, making it a robust choice for privacy-preserving synthetic data generation. Compared to other state-of-the-art models, KIPPS exhibits:
- High predictive accuracy and better distributional similarity to the original data.
- Competitive performance in downstream machine learning tasks, closely matching the results of models trained on real data.
- Strong resilience against membership inference and attribute inference attacks, ensuring the privacy of the synthetic data.
The KIPPS framework's ability to generate high-quality, privacy-preserving synthetic data while addressing domain-specific challenges highlights its potential for wide-ranging applications in data-driven research and analysis.
統計資料
The rapid evolution of machine learning has led to a large demand for data across diverse domains.
Data sharing is often restricted due to privacy and confidentiality concerns, necessitating the need for privacy-preserving measures.
Synthetic data generation offers a scalable solution to this problem, allowing organizations to collaborate on research and analysis without compromising sensitive information.
引述
"Synthetic data generation stands out as a crucial solution in the face of growing concerns about privacy. Rather than sharing raw, identifiable data, organizations can generate synthetic samples that retain the overall structure and patterns of the data."
"Generative Deep Learning has emerged as a powerful tool for enhancing privacy in various applications, addressing concerns related to data security and confidentiality."