# Securely Analyzing Mixed Data with Distributed AI and Privacy > This patent describes a method for securely analyzing different types of data, like images and text, across multiple computers without collecting all the data in one place, while also protecting privacy. - **Patent:** US 20250184362 - **Original title:** Federated mining method and system for multimodal data based on multiple security policies - **Owner:** Xuzhou Medical College - **Status:** Active - **Times cited:** 14 - **Field:** biotech, software, telecommunications, ai_ml, consumer_electronics ## What it does This patent outlines a method for analyzing diverse data, known as multimodal data, using a distributed approach called federated learning (S1). It sets up a system where multiple computers, called local edge nodes, can work together on data without sharing the raw information. Before participating, each node undergoes a 'multiple authentication mechanism' (S2), which includes a lightweight trusted verification using a random forest model and a calculation of its 'local model reputation' based on its F1_score and error rate. The system then performs 'multimodal data fusion' and 'multimodal data classification' on the combined dataset using a 'generalized multimodal data feature fusion model' that relies on a 'multi-head attention mechanism' (S3). This mechanism involves extracting features (e.g., 3D-CNN for images, OpenSmile for audio, Word2Vec for text), splicing them, calculating correlation scores, and using these scores to weight and sum representations for fusion and dimensionality reduction. Finally, an 'adaptive perturbation mechanism' (S4) adds noise dynamically to protect privacy, using cyclic correlation analysis and differential privacy with Gaussian noise. ## What it does NOT cover - Does not cover data mining systems that collect all raw data into a central location before processing. - Does not cover systems that rely on only a single method for verifying the trustworthiness of participating data nodes. - Does not cover analysis of data that consists of only one type, such as just images or just audio. - Does not cover privacy protection methods that add noise without dynamically adjusting it based on data correlations and differential privacy. - Does not cover data fusion models that do not specifically use a multi-head attention mechanism for combining different data types. ## The clever bit The clever bit is combining federated learning with a robust, multi-layered security and privacy framework specifically designed for multimodal data. This includes not just authenticating nodes, but also evaluating their 'reputation' and dynamically adding noise to protect privacy based on data correlations, ensuring both collaboration and data confidentiality. ## Real-world examples 1. Medical diagnostic systems analyzing patient images, audio, and text notes from different hospitals. 2. Smart city applications processing sensor data, video feeds, and text reports from various city departments. 3. Industrial IoT platforms combining machine sensor data, maintenance logs, and operator voice commands. 4. Personalized health monitoring apps analyzing wearable data, user input, and environmental factors. ## Why it matters This technology is important because it allows powerful AI models to learn from sensitive data, like medical records, without compromising patient privacy. By keeping data localized and only sharing model updates, it addresses critical concerns in industries with strict data regulations. This approach can accelerate the development of AI applications in fields where data sharing is otherwise difficult or impossible. ## Frequently asked questions ### What does Securely Analyzing Mixed Data with Distributed AI and Privacy cover? This patent describes a method for securely analyzing different types of data, like images and text, across multiple computers without collecting all the data in one place, while also protecting privacy. ### Who owns patent US 20250184362? This patent is owned by Xuzhou Medical College. ### When does this patent expire? This patent is expected to expire on December 30, 2044, when the invention enters the public domain. ### What is patent US 20250184362 cited by? This patent has been cited by 14 later patents that build on its ideas. ### What problem does this patent solve? This technology is important because it allows powerful AI models to learn from sensitive data, like medical records, without compromising patient privacy. By keeping data localized and only sharing model updates, it addresses critical concerns in industries with strict data regulations. This approach can accelerate the development of AI applications in fields where data sharing is otherwise difficult or impossible. ### What does this patent NOT cover? Does not cover data mining systems that collect all raw data into a central location before processing. **Full plain-English explainer:** https://patentbrief.org/patent/us/20250184362/federated-mining-method-and-system-for-multimodal-data-based-on-multiple-securit **Original patent:** https://patents.google.com/patent/US20250184362 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How AI Models Learn from Your Health Data Without Sharing It](https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application) — This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data. - [How Devices Securely Share Machine Learning Model Updates](https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus-) — This patent describes a method for multiple devices to train a machine learning model using their own data and then securely share parts of the trained model with each other before sending it to a central server, improving data privacy. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model. - [How Training Systems Share AI Knowledge Without Sharing Private Student Data](https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems) — This patent describes a system where different student training centers can improve their AI models by sharing general learning patterns, not private student data, to make training better for everyone.