High-Speed Cross-Entity Data Processing via Secret-Shared MPC
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Solution Overview
Problem
Existing methods struggle to analyze online activity data across multiple entities without compromising user privacy, as entities are reluctant to share sensitive user interaction data.
Innovation Solution
A method involving multi-party computation (MPC) and secret sharing is employed, where each entity creates secret shares of a multi-register sketch representing content presentations, which are analyzed by an MPC group without revealing individual values, and results are combined with random noise to protect privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities share user interaction data for analysis, then analysis accuracy improves, but user privacy protection deteriorates
Solution Approach 1:
The patent segments user interaction data into multiple secret shares distributed across different entities. Each entity holds only a portion of the data (a secret share), and no single entity can reconstruct the original data alone. This segmentation enables collaborative analysis while maintaining privacy, as the data is divided into independent parts that only become meaningful when combined through secure multi-party computation protocols.
Solution Approach 2:
The patent introduces secure multi-party computation (MPC) protocols as an intermediary mechanism that enables analysis without direct data sharing. The MPC framework acts as a trusted computation environment where entities can jointly compute analysis results on segmented data without exposing the underlying user interaction information to any single party, thus mediating between analysis needs and privacy protection.
2Object-affected harmful factors
If traditional encryption methods are used for privacy protection, then user privacy is protected, but processing speed deteriorates
Solution Approach 1:
The patent segments data into secret shares that can be processed independently and in parallel across multiple entities. This segmentation eliminates the need for sequential encryption/decryption operations required by traditional methods, allowing simultaneous computation on distributed data portions and significantly improving processing speed while maintaining privacy through the secret sharing mechanism.
Solution Approach 2:
The patent replaces traditional mechanical encryption and decryption operations with algebraic secret sharing operations and secure multi-party computation protocols. Instead of encrypting entire datasets before processing (which requires slow sequential decryption), the system uses mathematical operations on segmented data that can be computed efficiently in parallel, substituting heavy cryptographic mechanics with lighter algebraic operations.
3Object-affected harmful factors
If differential privacy is applied to protect user information, then privacy is maintained, but result accuracy deteriorates due to added noise
Solution Approach 1:
The patent segments user interaction data into fine-grained secret shares that capture detailed interaction patterns. By segmenting data at a granular level before analysis, the system can perform precise computations on the segmented data and then aggregate results, achieving both privacy protection and high accuracy without needing to add significant noise that would degrade result quality.
Solution Approach 2:
The patent performs preliminary segmentation of data into secret shares before the analysis process begins. This preliminary action structures the data in a way that enables accurate analysis on the segmented form, allowing the system to extract precise insights from the privacy-protected segmented data without requiring heavy noise addition that would otherwise be needed to protect privacy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving, from a content distributor, plan data specifying a set of distribution plans that cause distribution of content. Instructions are transmitted to publishers to submit secret shares of a multi-register sketch representing presentations of the content. A notification that the content distributor has requested an analysis of the presentations of the content is sent to a multi-party computing group. A result share of the analysis of the presentation of the content is received from multiple MPC devices in the MPC group. A set of result shares received from the of MPC devices are transmitted to the content distributor.