indsigt - Machine Learning - # Privacy-Preserving Synthetic Data Generation

Knowledge Infusion in Privacy-Preserving Synthetic Data Generation: The KIPPS Framework

Q: How can the KIPPS framework be extended to incorporate additional domain-specific knowledge, such as causal relationships or temporal dynamics, to further enhance the realism and utility of the generated synthetic data?

The KIPPS framework can be significantly enhanced by integrating additional domain-specific knowledge, particularly causal relationships and temporal dynamics. To incorporate causal relationships, the framework could utilize causal inference techniques and causal graphs that explicitly represent the relationships between variables. By embedding these causal structures into the Knowledge Graphs (KGs), KIPPS can guide the generative model to respect these relationships during the data generation process. This would ensure that the synthetic data not only reflects the statistical properties of the original data but also adheres to the underlying causal mechanisms, thereby improving the realism of the generated datasets. In terms of temporal dynamics, the KIPPS framework could be adapted to include temporal knowledge by integrating time-series analysis techniques. This could involve the use of temporal KGs that capture the evolution of relationships and attributes over time. By incorporating temporal constraints and patterns, such as seasonality or trends, the generative model can produce synthetic data that reflects realistic temporal behaviors. For instance, in healthcare data, the model could generate patient records that account for changes in health status over time, or in financial data, it could simulate stock prices that follow historical trends and volatility patterns. Overall, these enhancements would lead to more realistic and utility-rich synthetic datasets, making them more applicable for downstream tasks in various domains.

Q: What are the potential limitations of the current differential privacy techniques used in the KIPPS framework, and how could they be improved to provide stronger privacy guarantees without compromising data utility?

The current differential privacy techniques employed in the KIPPS framework, particularly the Differentially Private Stochastic Gradient Descent (DP-SGD), have several limitations. One significant challenge is the trade-off between privacy and utility; adding noise to the model can degrade the quality of the synthetic data, leading to less accurate representations of the original dataset. This noise addition can obscure important patterns and relationships, ultimately affecting the performance of downstream machine learning tasks. To improve the privacy guarantees without compromising data utility, several strategies could be implemented. First, adaptive noise mechanisms could be explored, where the amount of noise added is dynamically adjusted based on the sensitivity of the data and the specific attributes being generated. This would allow for a more nuanced approach to privacy, preserving utility in less sensitive areas while still providing strong privacy protections where needed. Additionally, incorporating advanced privacy-preserving techniques such as Local Differential Privacy (LDP) could enhance privacy guarantees. LDP allows individuals to add noise to their data before it is shared, ensuring that the data remains private even if the central server is compromised. This could be particularly useful in scenarios where sensitive attributes are involved. Lastly, employing ensemble methods that combine multiple differentially private models could also enhance privacy. By aggregating outputs from various models, the overall privacy risk can be reduced while maintaining a high level of data utility. These improvements would help strike a better balance between privacy and utility in the KIPPS framework.

Kernekoncepter

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.

Resumé

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:

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.
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.
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.

Tilpas resumé

Genskriv med AI

Generer citater

Oversæt kilde

Til et andet sprog

Generer mindmap

fra kildeindhold

Besøg kilde

arxiv.org

Statistik

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.

Citater

"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."

Vigtigste indsigter udtrukket fra

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation

by Anantaa Kota... kl. arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17315.pdf

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation

Dybere Forespørgsler

How can the KIPPS framework be extended to incorporate additional domain-specific knowledge, such as causal relationships or temporal dynamics, to further enhance the realism and utility of the generated synthetic data?

The KIPPS framework can be significantly enhanced by integrating additional domain-specific knowledge, particularly causal relationships and temporal dynamics. To incorporate causal relationships, the framework could utilize causal inference techniques and causal graphs that explicitly represent the relationships between variables. By embedding these causal structures into the Knowledge Graphs (KGs), KIPPS can guide the generative model to respect these relationships during the data generation process. This would ensure that the synthetic data not only reflects the statistical properties of the original data but also adheres to the underlying causal mechanisms, thereby improving the realism of the generated datasets.
In terms of temporal dynamics, the KIPPS framework could be adapted to include temporal knowledge by integrating time-series analysis techniques. This could involve the use of temporal KGs that capture the evolution of relationships and attributes over time. By incorporating temporal constraints and patterns, such as seasonality or trends, the generative model can produce synthetic data that reflects realistic temporal behaviors. For instance, in healthcare data, the model could generate patient records that account for changes in health status over time, or in financial data, it could simulate stock prices that follow historical trends and volatility patterns. Overall, these enhancements would lead to more realistic and utility-rich synthetic datasets, making them more applicable for downstream tasks in various domains.

What are the potential limitations of the current differential privacy techniques used in the KIPPS framework, and how could they be improved to provide stronger privacy guarantees without compromising data utility?

The current differential privacy techniques employed in the KIPPS framework, particularly the Differentially Private Stochastic Gradient Descent (DP-SGD), have several limitations. One significant challenge is the trade-off between privacy and utility; adding noise to the model can degrade the quality of the synthetic data, leading to less accurate representations of the original dataset. This noise addition can obscure important patterns and relationships, ultimately affecting the performance of downstream machine learning tasks.
To improve the privacy guarantees without compromising data utility, several strategies could be implemented. First, adaptive noise mechanisms could be explored, where the amount of noise added is dynamically adjusted based on the sensitivity of the data and the specific attributes being generated. This would allow for a more nuanced approach to privacy, preserving utility in less sensitive areas while still providing strong privacy protections where needed.
Additionally, incorporating advanced privacy-preserving techniques such as Local Differential Privacy (LDP) could enhance privacy guarantees. LDP allows individuals to add noise to their data before it is shared, ensuring that the data remains private even if the central server is compromised. This could be particularly useful in scenarios where sensitive attributes are involved.
Lastly, employing ensemble methods that combine multiple differentially private models could also enhance privacy. By aggregating outputs from various models, the overall privacy risk can be reduced while maintaining a high level of data utility. These improvements would help strike a better balance between privacy and utility in the KIPPS framework.

Given the versatility of the KIPPS framework, how could it be adapted to generate synthetic data for other domains, such as finance or social sciences, and what unique challenges might arise in those contexts?

The KIPPS framework's adaptability allows it to be tailored for generating synthetic data across various domains, including finance and social sciences. In finance, the framework could be modified to incorporate financial-specific knowledge, such as market trends, economic indicators, and regulatory constraints. By integrating financial KGs that capture relationships between different financial instruments, market behaviors, and economic conditions, KIPPS can generate synthetic datasets that reflect realistic financial scenarios, such as stock price movements, trading volumes, and risk assessments.
In social sciences, the framework could be adapted to account for complex social interactions, demographic factors, and cultural influences. By utilizing social KGs that represent relationships among individuals, groups, and societal structures, KIPPS can generate synthetic data that captures social dynamics, such as voting behavior, social mobility, and community interactions.
However, unique challenges may arise in these contexts. In finance, the high volatility and non-linear relationships present in financial data can complicate the modeling process, requiring sophisticated techniques to accurately capture these dynamics. Additionally, regulatory compliance and ethical considerations in finance necessitate stringent privacy measures, which could further complicate the data generation process.
In social sciences, the challenge lies in the representation of complex social phenomena and the need for rich contextual information. The diversity of social behaviors and the influence of external factors can make it difficult to create representative synthetic datasets. Moreover, ensuring that the generated data does not reinforce biases present in the original data is crucial, requiring careful consideration of fairness and equity in the data generation process.
Overall, while the KIPPS framework can be effectively adapted for these domains, addressing the unique challenges associated with each will be essential for producing high-quality, realistic synthetic data.