Key Mapping for Secure User Data Deletion in NLP Systems
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Solution Overview
Problem
Existing speech recognition systems face challenges in efficiently and securely deleting user data across multiple interconnected devices and systems, particularly in compliance with regulatory requirements, while maintaining system functionality and user privacy.
Innovation Solution
A flexible data tracking and deletion system that uses input and output identifiers to generate and send delete commands, allowing for surgical deletion of user data from natural language processing systems and target systems without disrupting other user or device functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If user data is deleted from natural language processing systems, then user privacy and regulatory compliance are improved, but system functionality and data availability deteriorate
Solution Approach 1:
The patent segments data identifiers into two distinct types: known keys (exposed to users) and internal keys (used by target systems). This segmentation allows selective deletion where users can request deletion using known keys without exposing or affecting internal keys, thus maintaining system functionality while protecting user privacy. The data structure is divided into user-accessible portions and system-internal portions that remain intact.
Solution Approach 2:
The patent introduces an intermediary data structure (the association table with known keys and internal keys) that mediates between user deletion requests and target system data. When a user provides a known key, the system uses it as an intermediary to locate and delete the corresponding internal key data in target systems, without directly exposing internal keys to users. This intermediary mechanism enables privacy protection while preserving system integrity.
2Object-affected harmful factors
If data deletion is implemented across multiple interconnected systems, then regulatory compliance is improved, but system complexity and coordination requirements worsen
Solution Approach 1:
The patent creates a universal data structure (the data structure with known keys and internal keys) that serves multiple functions across different target systems. This single data structure enables the same deletion mechanism to work across natural language processing systems, skill systems, and other interconnected systems, simplifying coordination while ensuring comprehensive regulatory compliance across all systems.
Solution Approach 2:
The patent implements a feedback mechanism where target systems send acknowledgments back to the natural language processing system after receiving deletion commands. This feedback loop ensures that deletion requests are properly propagated and executed across all interconnected systems, providing verification of compliance without requiring complex coordination protocols. The acknowledgment system creates a closed-loop control mechanism that simplifies multi-system coordination.
3Manufacturing precision
If precise data deletion is performed using identifier mappings, then data deletion accuracy is improved, but data structure complexity worsens
Solution Approach 1:
The patent segments identifiers into known keys and internal keys with distinct roles. Known keys serve as precise, user-friendly identifiers for deletion requests, while internal keys maintain precise system-internal data identification. This segmentation enables accurate data deletion based on user input without requiring the system to manage complex identifier transformations, as each key type has a dedicated function.
Solution Approach 2:
The patent creates a simplified copy (known key) of the internal identifier (internal key) that can be safely exposed to users. This copy mechanism allows precise data deletion by matching known keys against internal keys in the data structure, without requiring users to handle or understand the complexity of internal system identifiers. The copying approach maintains deletion precision while hiding data structure complexity from users.
Data Source
AI summary
Described are techniques for tracking associations between known keys and internal keys related to user data received at a natural language processing system and shared with target systems. The system can receive a request to delete data associated with a user or device, and determine one or more known keys related to the request. The system can retrieve previously stored associations between known keys and internal keys, and use the associations to generate a delete command containing relevant internal keys to be sent to the target systems, which in turn can delete data associated with the internal keys.