Privacy Query Layer for Sensitive Data Without Raw Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies for managing and sharing sensitive datasets fail to provide adequate privacy while maintaining utility, as they either breach individual privacy or are vulnerable to attacks, and lack flexibility and usability for industry usage.
Innovation Solution
A computer-implemented system that identifies and protects sensitive data through tokenization, masking, and k-anonymization, enabling privacy-preserving aggregate queries and computations, while controlling data access and logging usage for malicious behavior, and allowing data to be joined against common identifiers without revealing them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access control and secure enclaves are used to provide access to sensitive data, then data security is improved, but individual privacy is breached because analysts can still learn sensitive information about individuals
Solution Approach 1:
The patent introduces privacy-enhancing technology as an intermediary layer between the sensitive data and the data analyst. This intermediary processes queries and returns results that preserve individual privacy while still enabling group-level analysis. The PHT acts as a mediator that transforms raw sensitive data into privacy-preserving representations, allowing analysts to conduct business intelligence, reporting, and cohort analyses without accessing individual-level sensitive information.
2Object-affected harmful factors
If query set size restriction is used to suppress aggregate results for small populations, then some privacy protection is achieved, but the approach is insufficient because tracker attacks can circumvent it and recover individual information
Solution Approach 1:
The patent changes the fundamental parameter of privacy protection from simple query suppression based on result size to a more robust differential privacy mechanism. Instead of merely suppressing queries that return small result sets, the system applies mathematical transformations and noise calibration to ensure that individual contributions cannot be discerned from aggregate results, even when analysts perform multiple queries or combine results with external information.
3Object-affected harmful factors
If privacy-preserving query interfaces with strong differential privacy guarantees are implemented, then individual privacy is protected, but the systems lack flexibility and usability for industry applications
Solution Approach 1:
The patent introduces dynamic and configurable privacy-enhancing technology that can adapt to different industry use cases, data types, and privacy requirements. The system allows flexible configuration of privacy parameters, supports multiple query interfaces and data formats, and can be tuned to provide appropriate levels of privacy protection while maintaining usability. This dynamic approach enables the same core technology to serve diverse applications from healthcare to finance with customized privacy settings.
4Object-affected harmful factors
If sensitive data is anonymized through processing to produce safe copies, then privacy protection is improved, but data utility may be reduced
Solution Approach 1:
The patent applies controlled parameter changes to the data through differential privacy mechanisms that add calibrated noise or apply mathematical transformations. These transformations are designed to preserve statistical properties and group-level patterns while obscuring individual-level information. The degree of transformation can be tuned to balance privacy protection with data utility, allowing analysts to maintain the ability to conduct meaningful business intelligence and cohort analyses on anonymized data.
Data Source
AI summary
A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.