Summary of Silofuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models, by Aditya Shankar et al.
SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models
by Aditya Shankar, Hans Brouwer, Rihan Hai, Lydia Chen
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Databases (cs.DB); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 SiloFuse is a novel generative framework for synthesizing high-quality tabular data across multiple silos without compromising privacy. Traditional data synthesizers are designed for centrally stored data, but real-world scenarios often involve distributed features across silos. SiloFuse utilizes a distributed latent tabular diffusion architecture to ensure privacy by learning latent representations for each client’s features and masking actual values. The framework employs stacked distributed training to improve communication efficiency, reducing the number of rounds to a single step. Experimental results on nine datasets show that SiloFuse outperforms centralized diffusion-based synthesizers in resemblance and utility, achieving 43.8% and 29.8% higher percentage points over GANs. Additionally, SiloFuse proves robust to feature permutations and varying numbers of clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where different companies or organizations have their own data that they don’t want to share with others. This can make it hard for them to work together or get new insights from each other’s data. A team of researchers has created a new way to solve this problem by generating fake but realistic data that doesn’t reveal any secrets. They call this method SiloFuse, and it works by taking pieces of information from different sources and combining them into something new. This allows companies to share their data without giving away too much. The team tested SiloFuse on nine different sets of data and found that it worked better than other methods at making fake data that was similar to the real thing. They also showed that SiloFuse could withstand changes in how the data was organized or who had access to it. |
Keywords
* Artificial intelligence * Diffusion
