Garbled Circuit Desensitization for Privacy-Preserving Analytics
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Solution Overview
Problem
Current methods for desensitizing sensitive data struggle to balance preserving individual user privacy and retaining the maximum value of input data, often losing data values due to stringent regulations like GDPR, and are complex and inefficient.
Innovation Solution
A computer-implemented method and system that performs strong desensitization within a garbled circuit, compiling a program into two matching halves, allowing for analytics functions to be executed using tokenized data, with optional statistical desensitization and encryption to ensure privacy and prevent re-identification, while maintaining data integrity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional desensitization methods are used to comply with regulations like GDPR, then individual user privacy is protected, but valuable input data is lost and data analysis capability is reduced
Solution Approach 1:
The patent introduces garbled circuits as an intermediary computational framework that enables privacy-preserving data analysis. The garbled circuit acts as a mediator between the data owner and analytics provider, allowing computations to be performed on encrypted data without revealing either the input data or the computation logic to either party, thus protecting privacy while preserving data value
Solution Approach 2:
The patent transforms data from plaintext to encrypted form using garbled circuit encoding, changing the parameter of data representation. This parameter change allows the data to maintain its analytical value while becoming unreadable and unusable for unauthorized parties, effectively protecting privacy without losing information
2Adaptability or versatility
If traditional desensitization methods are used to remove personally identifiable information, then data can be shared freely, but the desensitization process is complex and inefficient
Solution Approach 1:
The patent replaces traditional mechanical/manual desensitization processes with an automated garbled circuit-based system. The garbled circuit automatically performs privacy-preserving computations through cryptographic operations, eliminating the need for complex manual data cleaning and anonymization processes, thus reducing complexity while maintaining data shareability
3Reliability
If stringent privacy controls are implemented on data containing personal information, then individual privacy is protected, but data analysis and diagnostics are prevented or limited
Solution Approach 1:
The patent adds a cryptographic dimension to data processing by implementing garbled circuits. This new dimension allows computations to occur in an encrypted space, enabling privacy protection and data analysis to coexist by operating in a previously unavailable computational realm where both confidentiality and utility are maintained
Data Source
AI summary
A method, system, and computer program product for performing strong desensitization of sensitive data within a garbled circuit includes: compiling a predetermined program into a first program, where the compiled first program is encoded in a form of a garbled circuit, and where the predetermined program runs on sensitive data; and executing the first program, where executing the first program includes: executing an analytics function using tokenized data with a first set of sensitive information and analytics data with a second set of sensitive information, where the tokenized data originated from a data provider and the analytics data originated from an analytics provider; and generating an output of the first program using a result of the analytics function, where the output contains desensitized data.