Secure MPC Content Selection to Reduce Bandwidth and Data Leakage
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Solution Overview
Problem
Existing content selection processes in secure multi-party computation (MPC) systems face challenges in maintaining user privacy and data integrity, particularly when performing final selection processes at client devices, which lead to increased bandwidth consumption, latency, and potential leakage of sensitive information.
Innovation Solution
A secure MPC process is performed by a cluster of servers that operate as separate entities, using secret sharing and cryptographic techniques to select digital components based on user information without accessing it in cleartext, reducing the need for transmissions and ensuring privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the final selection process is performed at the client device, then the client device can independently select content, but bandwidth consumption increases and latency is introduced due to additional transmissions
Solution Approach 1:
The selection process is segmented into two parts: the MPC cluster performs preliminary selection and generates candidate results, while the client device performs final selection. This segmentation allows the client to have independence in the final decision while reducing the computational burden and transmission requirements compared to performing the entire selection process at the client.
Solution Approach 2:
The MPC cluster performs preliminary selection actions by pre-processing candidate content and generating selection results before transmitting them to the client device. This preliminary action reduces the amount of data that needs to be transmitted and processed at the client, thereby reducing bandwidth consumption and latency while maintaining client independence for the final selection.
2Measurement precision
If user information is transmitted in cleartext for selection processing, then the selection process can be performed accurately, but data leakage risk increases
Solution Approach 1:
Secret sharing technology is introduced as an intermediary mechanism between the client device and the MPC cluster. The user information is split into multiple secret shares that are processed by the MPC cluster without being reconstructed in cleartext. This intermediary mechanism enables accurate selection processing while preventing data leakage, as the MPC cluster can perform computations on the secret shares without accessing the actual user information.
Solution Approach 2:
The state of user information is changed from cleartext to secret share representation. By transforming the data format from readable text to cryptographic secret shares, the system enables processing operations while maintaining security. The MPC cluster operates on these transformed parameters (secret shares) to achieve selection accuracy without exposing the underlying user information.
3Stability of the object's composition
If multiple transmissions are performed between client device and MPC cluster, then data consistency can be maintained, but latency increases
Solution Approach 1:
The MPC cluster performs preliminary selection and prepares candidate results before the client device needs them. By advancing the selection process to occur before the actual content delivery, the system reduces the number of back-and-forth transmissions needed during critical content delivery moments, thereby maintaining data consistency while reducing latency.
Data Source
AI summary
This document describes systems and techniques for improving the integrity and protecting the security of information in content selection and distribution. In one aspect, a method includes receiving, by a first server of a secure multi-party computation (MPC) system from an application on a user device, a request for a digital component. The request is parsed into distinct sub-requests. Each sub-request is transmitted to a different server. A set of candidate selection values is received from a separate server. The first server performs, in collaboration with one or more second servers of the MPC system, a selection process to generate a selection result for a winning digital component, including merging, the first set of candidate selection values and a set of cached selection values to create a final set of candidate selection values and sorting the final set according to the values of the candidate selection values.