User ID Space Reconciliation via Hash Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reconciling user identifier spaces across different computing systems is challenging due to their large size and the lack of technical resources, especially when these systems have limited capabilities and must maintain user anonymity, making it difficult to identify overlapping user accounts without sharing sensitive personal identifiable information.
Innovation Solution
A method and system that determine common categories of information in both user spaces, generate hash values using a hash function applied to this information, and create mapping information between the user spaces based on matching hash values and corresponding user identifiers, allowing for the reconciliation of user spaces without accessing personally identifiable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional user ID reconciliation methods are used, then user identification accuracy is improved, but data privacy is compromised and system complexity increases
Solution Approach 1:
A third-party reconciliation system is introduced as an intermediary between the first and second computing systems. This mediator performs the user ID reconciliation by hashing user identifiers and comparing hash values, enabling accurate user identification without requiring the participating systems to share sensitive user data. The intermediary assumes the computational burden, allowing the original systems to maintain data privacy while achieving accurate reconciliation.
2Measurement precision
If user identifier spaces are reconciled by sharing data, then reconciliation accuracy is improved, but computational resources are consumed and user anonymity is compromised
Solution Approach 1:
Only the essential element needed for reconciliation - the user identifier - is extracted and hashed. The actual user data (names, contact information, etc.) remains in place at each computing system and is not shared. By extracting only the minimal necessary information and transforming it through hashing, the system achieves accurate reconciliation without transferring large volumes of sensitive data between systems.
3Object-affected harmful factors
If hash functions are applied to user information, then user anonymity is preserved, but computational complexity increases
Solution Approach 1:
Each computing system independently applies the hash function to its own user identifiers using a shared secret key. This self-service approach means that hashing is performed locally at each system rather than requiring centralized processing. The computational complexity is distributed across multiple systems, and the use of a shared secret key allows efficient verification without requiring complex cryptographic operations at any single location.
Data Source
AI summary
Two different user spaces can be mapped to each other based on one or more categories of information that are common to both. The mapping is based on hash values generated by applying the same hash function to the same information of the categories of information that identifies users in each user space.