Secure Multi-Party Neural Training With Hidden Intersection IDs
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Solution Overview
Problem
Existing secure multi-party computing protocols expose customer IDs during private set intersection, compromising data privacy and security in vertical secure multi-party learning.
Innovation Solution
Implement a method where computing participants use secret sharing and oblivious programmable pseudorandom functions to determine and protect intersection IDs, sharing feature values only for AI model training, and delete non-intersection data to enhance security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If private set intersection technology is used to calculate intersection of IDs, then data alignment for vertical secure multi-party learning is achieved, but ID privacy is exposed and security deteriorates
Solution Approach 1:
The patent segments the ID and feature value into separate processing streams. IDs are processed through PSI to determine intersection without exposing plaintext, while feature values are processed separately through secret sharing. This segmentation allows data alignment functionality while preventing ID privacy exposure.
Solution Approach 2:
The patent introduces ciphertext data and secret sharing as intermediary mechanisms. Instead of directly exposing intersection IDs, the system uses ciphertext indicators and shared secrets as intermediaries to convey intersection information without revealing the actual ID values, thus maintaining privacy while enabling data alignment.
2Productivity
If all computing parties know specific IDs in the intersection, then data alignment is completed, but ID privacy of customers is exposed and security becomes poor
Solution Approach 1:
The patent creates a copy of the intersection identification process using ciphertext data and secret sharing. Instead of directly revealing intersection IDs, the system generates encrypted indicators and shared secret values that can be used to identify intersection records without exposing the actual ID information, thus maintaining privacy while preserving alignment efficiency.
3Reliability
If feature values are shared using secret sharing mode, then data privacy is protected during transmission, but computational complexity increases
Solution Approach 1:
The patent applies secret sharing selectively only to feature values that need to be protected, rather than applying it universally to all data processing. The secret sharing is implemented locally at each computing party's end for the specific feature value transmission, providing targeted privacy protection with minimized computational overhead.
Data Source
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AI summary
A data processing method and system, and a computing device are provided. The method includes: A first computing participant and a second computing participant separately obtain first ciphertext data. The second computing participant obtains, from the first computing participant, a share of a first feature value corresponding to a first ID. The first computing participant obtains second data, where if the first ID is an intersection ID, the second data is a share of a second feature value that is in the second computing participant and that corresponds to the first ID, or if the first ID is not an intersection ID, the second data is a random number. If the first ciphertext data indicates that the first ID is an intersection ID, the first computing participant uses the second data and a share of the first feature value that is held by the first computing participant as training data of a neural network, and the second computing participant uses a share of the first feature value and a share of the second feature value that are held by the second computing participant as training data of the neural network. In the method, security of multi-party computing can be improved.