Privacy-Preserving Data Relationship Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in computing a relationship between private data from different entities while preserving privacy and preventing inter-entity data sharing, as entities are reluctant to share sensitive information due to privacy and liability concerns.
Innovation Solution
A method is developed to determine a representation of the intersection of member lists from different entities without accessing the lists directly, using one-way hash functions to identify common members and compute coefficients of a numeric relationship between features and salaries without revealing the actual data, employing a server to perform computations and provide digital transmissions of these coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities share sensitive data to compute relationships, then computation accuracy is improved, but data privacy and security deteriorate
Solution Approach 1:
A trusted third-party server acts as an intermediary to perform the computation of relationships between features and salaries. The server receives encrypted data from both entities, computes the relationship coefficients without accessing the actual data values, and returns the results. This mediator approach enables accurate computation while preserving data privacy, as neither entity directly shares sensitive information with the other.
Solution Approach 2:
The patent transforms the computation from operating on raw data values to operating on encrypted representations and statistical aggregates. By changing the parameter space from individual data points to relationship coefficients computed over encrypted datasets, the system achieves computation accuracy while preventing access to underlying sensitive information.
2Measurement precision
If entities share data to identify common members, then intersection accuracy is improved, but data security deteriorates
Solution Approach 1:
Instead of sharing actual member lists, entities share cryptographic copies or representations (such as hashed identifiers or encrypted sets) that allow the server to compute the intersection. The server can determine common members by comparing these representations without accessing the original sensitive data, thereby maintaining data security while achieving accurate intersection identification.
3Productivity
If a server accesses private data to compute relationships, then computation capability is improved, but privacy preservation deteriorates
Solution Approach 1:
The server functions as a privacy-preserving intermediary that performs computation on encrypted data or cryptographic representations rather than accessing actual private information. This enables the server to utilize full computation capability to analyze relationships between features and salaries while the cryptographic layer prevents any loss or exposure of privacy-sensitive information.
Data Source
AI summary
Aspects of the present disclosure relate to cryptography. In particular, example embodiments relate to computing a relationship between private data of a first entity and private data of a second entity, while preserving privacy of the entities and preventing inter-entity data sharing. A server includes a first component to compute an intersection of two datasets, without directly accessing either dataset. The server includes a second component to compute a relationship, such as a regression, between data in the first dataset and data in the second dataset, without directly accessing either dataset.