Summary of Kipps: Knowledge Infusion in Privacy Preserving Synthetic Data Generation, by Anantaa Kotal and Anupam Joshi
KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
by Anantaa Kotal, Anupam Joshi
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed KIPPS framework addresses challenges in generating synthetic data for critical domains like Cybersecurity and Healthcare. Generative Deep Learning models struggle with discrete and non-Gaussian features, limited datasets, and attribute constraints. To overcome these issues, KIPPS infuses Domain and Regulatory Knowledge from Knowledge Graphs into generative models. The novel framework enhances the training process by providing supplementary context about attribute values and enforcing domain constraints. This approach leads to the generation of realistic and domain-compliant synthetic data. Evaluation on real-world datasets demonstrates the model’s effectiveness in balancing privacy preservation and data accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative Deep Learning models are used to create fake data, but this can be a problem when it comes to sensitive information like healthcare records or cybersecurity threats. The issue is that these models aren’t very good at following rules and constraints, which can lead to inaccuracies in the synthetic data. To solve this problem, researchers have developed a new model called KIPPS that incorporates knowledge from specialized domains like healthcare and cybersecurity into the generation process. This helps ensure that the fake data is not only realistic but also compliant with important regulations and guidelines. |
Keywords
» Artificial intelligence » Deep learning » Synthetic data
