Multi-Channel Data Transfer Privacy via Hash Segmentation
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Solution Overview
Problem
Existing data management systems face challenges in ensuring privacy and consent management for personally-identifiable information (PII) across multiple channels of communication, where anonymized and consented data are not effectively separated, leading to potential breaches in user privacy and compliance issues.
Innovation Solution
A multi-channel data transfer system is implemented, using separate communication channels for anonymized and consented data, where a client device queries data servers to compute hash values for accessing and linking data, ensuring that anonymized data remains unlinkable to users without consent, and consented data is securely linked to users, with mechanisms for revoking consent and maintaining data separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If anonymized data and consented data are stored together in a single data pool, then data access and querying become simpler, but user privacy is compromised and consent management becomes impossible
Solution Approach 1:
The patent divides the data pool into two separate channels: an anonymized data pool and a consented data pool. This segmentation allows the system to maintain both privacy protection and data accessibility by storing different types of data in separate locations with different access controls.
Solution Approach 2:
The patent introduces hash values as an intermediary mechanism that links anonymized data to consented data without exposing direct connections. The hash value acts as a mediator that enables consent verification while maintaining the separation between identifiable and anonymized data.
2Productivity
If anonymized data is made fully accessible without consent verification, then data utility and analytics capability improve, but user consent requirements are violated
Solution Approach 1:
The patent implements dynamic consent management where the system can adapt between different data access modes based on user consent status. Data can be accessed from the anonymized pool when no consent is needed, and from the consented pool when specific analytics require user authorization.
Solution Approach 2:
The patent applies different access control qualities to different data pools. The anonymized data pool allows broad access for general analytics, while the consented data pool implements restricted access with consent verification, giving each pool the appropriate level of openness for its purpose.
3Reliability
If separate channels are used for anonymized and consented data, then privacy and consent management improve, but system complexity and data querying overhead increase
Solution Approach 1:
The patent combines the two separate data pools under a unified query processing system that automatically determines which pool to access based on the query type and consent status. This merging at the query level reduces the operational complexity of managing separate systems while maintaining the security benefits of separation.
4Reliability
If hash values are computed for every data query to maintain privacy, then user identity protection improves, but query processing time and computational resources increase
Solution Approach 1:
The patent performs hash value computation in advance when data is inserted into the anonymized pool, rather than computing hashes during every query operation. This preliminary hashing action stores the hash values with the data, enabling faster query processing while maintaining identity protection.
Data Source
AI summary
Managing data transfer and privacy via a multi-channel transfer of information is provided. A query is received from a client device, the query indicating an access identifier corresponding to an entity for which data is stored. A consented data pool is accessed to identify a random value corresponding to the access identifier. A hash value is computed using a combination of both the access identifier and the random value. An anonymous data pool of stored data is queried to identify results from the stored data tagged with the hash value. The results are returned to the client device responsive to the query.