Secure MPC Bloom Filter User Group Selection
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Solution Overview
Problem
Current secure multi-party computation (MPC) systems face challenges in efficiently selecting digital components while preserving user privacy and reducing latency, as they often require large data transmission and high computational resources, which can lead to increased network bandwidth consumption and battery usage, especially on mobile devices.
Innovation Solution
The implementation of a secure MPC process that uses probabilistic data structures like Bloom filters and secret shares to determine user group membership and eligibility for digital components, reducing data transmission and computational resources by only sharing secret shares of selected components, thereby protecting user privacy and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If secure MPC systems use traditional cryptographic protocols for digital component selection, then user data privacy is protected, but network bandwidth consumption and battery usage increase due to large data transmission and high computational resources
Solution Approach 1:
The patent extracts only the essential elements needed for secure computation by using Bloom filters to represent user group memberships and interests. Instead of transmitting or processing complete user profiles or individual component data, the system works with condensed probabilistic representations, significantly reducing data volume while maintaining privacy and functionality
Solution Approach 2:
The system changes the parameter representation from exact matches to probabilistic membership indicators using Bloom filters. This allows the MPC protocol to operate on compressed data structures where user group memberships are represented as bit arrays rather than complete datasets, reducing computational complexity and energy consumption
2Reliability
If secure MPC systems use traditional cryptographic protocols for digital component selection, then user data privacy is protected, but latency increases due to large data transmission and high computational resources
Solution Approach 1:
The patent extracts only the essential elements needed for secure computation by using Bloom filters to represent user group memberships and interests. Instead of transmitting or processing complete user profiles or individual component data, the system works with condensed probabilistic representations, significantly reducing data volume while maintaining privacy and functionality
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing Bloom filter representations of user group memberships and digital component characteristics. When a selection request occurs, the MPC protocol operates on these pre-prepared data structures rather than processing raw data, reducing computation time and latency
3Measurement precision
If secure MPC systems transmit complete user data for component selection, then selection accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential elements needed for secure computation by using Bloom filters to represent user group memberships and interests. Instead of transmitting or processing complete user profiles or individual component data, the system works with condensed probabilistic representations, significantly reducing data volume while maintaining privacy and functionality
Data Source
AI summary
This document relates to using secure MPC to select digital components in ways that preserve user privacy and protects the security of data of each party that is involved in the selection process. In one aspect, a method includes receiving, by a first computing system of a secure MPC system and from a client device, a digital component request and a nonce. The first computing system generates, based on the nonce and a function, an array including a share of a Bloom filter representing user group identifiers for user groups that include a user of the client device as a member. For each of multiple user group identifiers, the first computing system calculates, in collaboration with one or more second computing systems of the secure MPC system and using the array, a respective first secret share of one or more user group membership condition parameters.