Cuckoo Filter–Based Secure MPC for Private User-Group Matching
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Solution Overview
Problem
Existing secure multi-party computation (MPC) systems fail to protect user privacy by allowing individual parties to access user data in cleartext, leading to potential breaches and increased bandwidth, latency, and processing power consumption.
Innovation Solution
Implementing secure MPC processes using probabilistic data structures, such as cuckoo filters, and distributed point functions to represent user group membership as secret shares, ensuring that user information is not accessed in cleartext by any single party, thereby reducing data size and transmission requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user information is transmitted in cleartext for digital component selection, then selection accuracy is improved, but user privacy is compromised and bandwidth consumption increases
Solution Approach 1:
The system segments user information into multiple secret shares distributed across different MPC parties. Each party holds only a portion of the data, and no single party can reconstruct the complete user information. This segmentation enables accurate digital component selection through cryptographic computation while preventing any individual party from accessing cleartext user data, thus resolving the contradiction between selection accuracy and privacy protection.
2Measurement precision
If complete user information is transmitted to MPC systems, then selection precision is improved, but bandwidth consumption and processing power increase
Solution Approach 1:
The system extracts only the essential selection criteria and contextual signals from complete user information, transforming them into compact cryptographic representations (secret shares and distributed point functions). This extraction process removes redundant data while preserving the ability to perform accurate digital component selection, thereby reducing bandwidth consumption and processing requirements without sacrificing selection precision.
3Loss of information
If user data is distributed across multiple MPC parties, then user privacy is protected, but system complexity increases
Solution Approach 1:
The system introduces standardized cryptographic intermediaries (secret sharing schemes and distributed point function evaluation protocols) that mediate between user privacy requirements and MPC computation needs. These intermediaries provide well-defined interfaces and procedures for distributing and computing on encrypted data, reducing the operational complexity despite the distributed architecture by offering reusable cryptographic building blocks.
Data Source
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AI summary
This document describes systems and techniques for protecting the security of information in content selection and distribution. In one aspect, a method includes receiving, by a first computing system of MPC systems, a digital component request including distributed point functions that represent a secret share of a respective point function that indicates whether a user of the client device is a member of a first user group. Selection values are identified. Each selection value corresponds to a respective digital component, a set of contextual signals, and a respective second user group identifier for a respective second user group to which the respective digital component is eligible to be distributed. A determination is made, for each selection value and using the distributed point functions in a secure MPC process, a candidate parameter that indicates whether the second user group identifier matches a user group that includes the user as a member.