Decentralized Data Aggregation via Secure Multi-Party Computation
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Solution Overview
Problem
Current data aggregation models face challenges in maintaining data privacy and security, as entities are hesitant to share data due to concerns about data breaches, privacy, and market share, making it difficult for parties to collaborate while protecting sensitive information.
Innovation Solution
A decentralized, secure, and privacy-preserving data aggregation system that uses secure multi-party computation (SMPC) to enable entities to compute and aggregate data without sharing confidential information, by fragmenting data into unrecognizable pieces that cannot be reassembled, allowing for secure computation across multiple entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities share confidential data to enable data aggregation and computation, then computational accuracy and result quality improve, but data privacy and security deteriorate
Solution Approach 1:
The patent segments confidential data into multiple encrypted fragments distributed across different computation nodes. Each node holds only a portion of the data in encrypted form, making it impossible to reconstruct the original data without all fragments. This segmentation enables computation on aggregated data while preserving privacy, as no single node has access to complete confidential information.
Solution Approach 2:
The patent introduces a secure multi-party computation protocol as an intermediary mechanism that enables computation without direct data sharing. The protocol acts as a mediator that processes encrypted data fragments from multiple entities, performs computations, and returns results without any entity exposing their raw confidential data to others.
2Object-affected harmful factors
If entities do not share confidential data to maintain privacy and security, then data protection improves, but the ability to aggregate and compute data deteriorates
Solution Approach 1:
The patent transforms data from its original readable form into encrypted mathematical representations (polynomial coefficients, secret shares). This parameter change allows data to be processed in encrypted form through secure multi-party computation protocols, enabling aggregation and computation while maintaining security. The data remains in a transformed state throughout the computation process.
Solution Approach 2:
The patent replaces the traditional mechanical approach of data sharing and centralized processing with a cryptographic system. Instead of physically sharing data files between entities, the system uses mathematical protocols (secret sharing, homomorphic encryption) to enable computation on encrypted data, substituting cryptographic operations for traditional data exchange mechanisms.
3Productivity
If data is centralized for processing, then computational efficiency improves, but security risks and vulnerability to breaches increase
Solution Approach 1:
The patent segments the centralized data processing model into a distributed architecture where computation nodes process encrypted data fragments locally. Each node performs computations on its portion of the encrypted data without needing to access or store complete datasets, distributing the computational workload and eliminating the security vulnerability of centralized data storage.
Solution Approach 2:
The patent introduces secure multi-party computation protocols as intermediaries that coordinate distributed computation without requiring central data aggregation. The protocol mediates the computation process by managing the exchange of encrypted intermediates between nodes, enabling efficient distributed processing while maintaining security through cryptographic guarantees.
Data Source
AI summary
Systems and methods for data aggregation and processing are provided in manner that is decentralized and preserves privacy. A data aggregation and computation system may include an interface, a controller, and one or more clusters of computation nodes. The interface may receive an inquiry from a requesting entity for computing information regarding an individual based on pieces of information held by a plurality of entities. The controller may communicate an identifier for the individual to a processor system associated with each of the entities based on the inquiry. The clusters of computation nodes may each receive encrypted data fragments from each of the processor systems, the data fragments comprising unrecognizable fragments that no individual processor system can re-assemble to recover the information, perform secure, multi-party computations based on the data fragments, and generate a result based on the secure, multi-party computations for the individual.