International business machines corporation (20240249153). PRIVACY ENHANCED FEDERATED TRAINING AND INFERENCE OVER VERTICALLY AND HORIZONTALLY PARTITIONED DATA simplified abstract

PRIVACY ENHANCED FEDERATED TRAINING AND INFERENCE OVER VERTICALLY AND HORIZONTALLY PARTITIONED DATA

Organization Name

international business machines corporation

Inventor(s)

Swanand Ravindra Kadhe of San Jose CA (US)

Heiko H. Ludwig of San Francisco CA (US)

Nathalie Baracaldo Angel of San Jose CA (US)

Yi Zhou of San Jose CA (US)

Alan Jonathan King of South Salem NY (US)

Keith Coleman Houck of Rye NY (US)

Ambrish Rawat of Dublin (IE)

Mark Purcell of Naas (IE)

Naoise Holohan of Maynooth (IE)

Mikio Takeuchi of Yokohama (JP)

Ryo Kawahara of Toshima-Ward (JP)

Nir Drucker of Zichron Yaakov (IL)

Hayim Shaul of Kfar Saba (IL)

PRIVACY ENHANCED FEDERATED TRAINING AND INFERENCE OVER VERTICALLY AND HORIZONTALLY PARTITIONED DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249153 titled 'PRIVACY ENHANCED FEDERATED TRAINING AND INFERENCE OVER VERTICALLY AND HORIZONTALLY PARTITIONED DATA

The patent application relates to systems, devices, computer program products, and/or computer-implemented methods for federated training and inferencing.

• The system includes a memory storing computer executable components and a processor executing these components.
• The modeling component trains an inferential model using horizontally and vertically partitioned data with a random decision tree.
• The inference component generates inferences in response to queries using the trained inferential model.
• First party private data is not directly shared with other parties to generate inferences.

• • Key Features and Innovation:**
• Federated training and inferencing system.
• Use of horizontally and vertically partitioned data.
• Training with a random decision tree.
• Protection of first party private data.

• • Potential Applications:**

This technology can be applied in various fields such as healthcare, finance, and marketing for secure and collaborative data analysis.

• • Problems Solved:**
• Secure training and inferencing with sensitive data.
• Collaborative data analysis without direct data sharing.
• Efficient model training with partitioned data.

• • Benefits:**
• Enhanced data privacy and security.
• Collaborative data analysis across multiple parties.
• Efficient and accurate inferencing with trained models.

• • Commercial Applications:**

This technology can be utilized in industries requiring secure and collaborative data analysis, such as healthcare analytics platforms and financial institutions for fraud detection.

• • Questions about Federated Training and Inferencing:**

1. How does the system ensure the privacy of first party private data? 2. What are the advantages of using a random decision tree for training the inferential model?

• • Frequently Updated Research:**

Stay updated on advancements in federated learning and secure data analysis techniques to enhance the efficiency and security of the system.

Original Abstract Submitted

systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to federated training and inferencing. a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a modeling component that trains an inferential model using data from a plurality of parties and comprising horizontally partitioned data and vertically partitioned data, wherein the modeling component employs a random decision tree comprising the data to train the inferential model, and an inference component that responds to a query, employing the inferential model, by generating an inference, wherein first party private data, of the data, originating from a first passive party of the plurality of parties, is not directly shared with other passive parties of the plurality of parties to generate the inference.