Enterprise Privacy Protection via Write-Time Data Hashing
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Solution Overview
Problem
Existing enterprise privacy solutions face challenges in protecting sensitive data while avoiding organizational complexity, computational costs, and stunting creativity. Current privacy-on-read approaches result in complex rules and roles, making data management and deletion inefficient.
Innovation Solution
Implementing a privacy-on-write approach by storing sensitive data in both hashed and encrypted forms, allowing for protected data recovery and deletion without direct access, thus reducing computational costs and organizational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy-on-read approaches are used to protect sensitive data, then data protection is improved, but organizational complexity and computational costs increase
Solution Approach 1:
The patent applies preliminary action by hashing sensitive data at the time of ingestion (write-time) rather than at access time (read-time). This upfront transformation eliminates the need for complex runtime privacy management, reducing organizational complexity while maintaining data protection. The data is hashed once during ingestion and remains protected throughout its lifecycle.
Solution Approach 2:
The patent extracts the privacy protection mechanism from the data access layer and moves it to the data ingestion layer. By separating the hashing operation from the access control layer, the system eliminates complex runtime privacy management requirements while maintaining robust data protection.
2Reliability
If privacy-on-read approaches are used to protect sensitive data, then data protection is improved, but data deletion efficiency deteriorates
Solution Approach 1:
The patent creates a hash copy of the sensitive data during ingestion. This hash copy serves as a permanent marker that can be efficiently deleted without affecting the original data structure. When data deletion is needed, only the hash copy needs to be removed, dramatically improving deletion efficiency while maintaining data protection.
Solution Approach 2:
The patent segments the data protection mechanism into two independent components: the hashed data itself and the hash copy. This segmentation allows the hash copy to be independently managed and deleted without complex operations on the main data structure, improving deletion efficiency.
3Reliability
If restrictive privacy policies are implemented, then consumer privacy is protected, but organizational adaptability and creativity are reduced
Solution Approach 1:
The patent implements self-service by automatically hashing sensitive data at ingestion without requiring ongoing privacy management decisions. This automation eliminates restrictive manual policies while maintaining strong privacy protection, allowing organizations to adapt freely without compromising consumer privacy.
Data Source
AI summary
Techniques for electronic privacy protection are disclosed. A first data record is received, including one or more sensitive data fields and one or more non-sensitive data fields. One or more encrypted data fields are generated by encrypting the one or more sensitive data fields using an encryption key. One or more hashed data fields are generated by hashing the one or more sensitive data fields using a hash function. A first mapping is stored, associating at least a portion of the first data record with the encryption key. A second data record is stored, including the one or more non-sensitive data fields, the one or more encrypted data fields, and the one or more hashed data fields.