Google llc (20240163341). PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION simplified abstract
Contents
- 1 PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION
Organization Name
Inventor(s)
Gang Wang of Frederick MD (US)
Marcel M. Moti Yung of New York NY (US)
PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240163341 titled 'PRIVACY PRESERVING CENTROID MODELS USING SECURE MULTI-PARTY COMPUTATION
Simplified Explanation
The patent application describes a method for a privacy-preserving machine learning platform that determines user groups to which to add a user based on user profile data, centroids, and a threshold distance.
- The method involves receiving a request for user group identifiers, user profile data, and a threshold distance from a client device.
- Centroids for user groups are identified based on a centroid model corresponding to a model identifier.
- A user group result is determined based on the user profile data, centroids, and threshold distance, indicating which user groups to add the user to.
Potential Applications
This technology could be applied in various industries such as healthcare, finance, and marketing for securely analyzing user data and making group-based decisions.
Problems Solved
This technology addresses the challenge of securely processing sensitive user data in a machine learning platform without compromising user privacy.
Benefits
The benefits of this technology include enhanced data privacy, improved user group identification accuracy, and secure data processing in multi-party computation systems.
Potential Commercial Applications
A potential commercial application of this technology could be in data analytics companies offering privacy-preserving machine learning services to clients in various industries.
Possible Prior Art
One possible prior art could be existing multi-party computation systems used for secure data processing in collaborative environments.
What are the potential security implications of implementing this technology?
Implementing this technology could raise concerns about the security of user data, especially in terms of data breaches or unauthorized access to sensitive information. Companies using this platform would need to ensure robust security measures are in place to protect user data.
How does this technology compare to existing user profiling methods in terms of accuracy and efficiency?
This technology offers a more privacy-preserving approach to user profiling compared to traditional methods, ensuring that user data is securely processed without compromising individual privacy.
Original Abstract Submitted
this disclosure relates to a privacy preserving machine learning platform. in one aspect, a method includes receiving, from a client device and by a computing system of multiple multi-party computation (mpc) systems, a first request for user group identifiers that identify user groups to which to add a user. the first request includes a model identifier for a centroid model, first user profile data for a user profile of the user, and a threshold distance. for each user group in a set of user groups corresponding to the model identifier, a centroid for the user group that is determined using a centroid model corresponding to the model identifier is identified. the computing system determines a user group result based at least on the first user profile data, the centroids, and the threshold distance. the user group result is indicative of user group(s) to which to add the user.
