Privacy-Preserving Queryable Database Using NLP Entity Abstraction
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Solution Overview
Problem
Existing queryable databases that provide aggregated data can be vulnerable to attacks where an attacker can deduce individual information by combining queries, even with differential privacy measures, as the noise generation algorithm can be reverse-engineered, compromising privacy.
Innovation Solution
A computer-implemented method using a trained Natural Language Processing (NLP) system to assign abstract named entities to free text documents with precision and recall values between 0.75 and 1, and a ratio of 0.7 to 1.3, eliminating the need for artificial noise generation, thereby ensuring privacy through natural recognition and disambiguation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy with artificial noise generation is used, then individual privacy is protected, but the noise generation algorithm can be reverse-engineered through multiple queries, compromising privacy
Solution Approach 1:
The patent extracts the noise generation process from the database query system itself and relocates it to the client side. The server only provides clean aggregated data, while each client independently generates noise based on their own private random seeds. This separation removes the vulnerable centralized noise generation algorithm that could be reverse-engineered, as the actual noise generation never occurs on the server where queries are processed.
Solution Approach 2:
The patent introduces client-side random seed generation as an intermediary between the database and the privacy protection mechanism. Instead of the server generating and managing noise directly, each client acts as an independent intermediary that generates its own noise locally. This intermediary layer prevents attackers from analyzing server responses to reverse-engineer the noise generation process, as each client's noise is independently generated and never revealed to the server.
2Measurement precision
If precision and recall are set between 0.75 and 1 with ratio 0.7 to 1.3, then acceptable accuracy is maintained, but perfect precision and recall cannot be achieved
Solution Approach 1:
The patent changes the fundamental parameter of how noise is introduced - not through adjustable precision/recall thresholds in traditional differential privacy, but through client-side random seed generation. The NLP system processes text with its inherent probabilistic nature, and the randomness is embedded in the client's local noise generation rather than being controlled by server-side parameters. This maintains accuracy while providing privacy through a different mechanism that doesn't require sacrificing precision.
Data Source
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AI summary
A computer-implemented method of generating a queryable database (109). The method receives a corpus of free text documents (120) containing confidential data, the free text documents being related to the same domain. A trained Natural Language Processing (NLP) system (104) assigns one or more abstract named entities to each free text document in the corpus. The abstract named entities of each free text document are stored in a queryable database configured to provide aggregated information regarding the named entities. The NLP system is configured such that the abstract named entities are recognised and disambiguated with a precision between 0.75 and less than 1 and a recall between 0.75 and less than 1, and such that the ratio of precision and recall is between 0.7 and 1.3; wherein the queryable database is free from the addition of artificial noise by an artificial noise generation algorithm.