Source Code Transformation for AI Privacy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The use of generative artificial intelligence (GAI) in software development poses a risk of intellectual property (IP) leakage, as proprietary source code provided to GAI can be inadvertently shared or learned by the AI, potentially leading to data leakage to competitors.
Innovation Solution
A method is introduced that transforms source code using a nonreversible one-way transform, converting sensitive information into nonce values that are meaningless to humans but retain the structure and flow of the code. This transformed code can be safely shared with third-party GAI providers, preventing IP leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If source code is provided to third-party GAI for software development assistance, then productivity and code quality improvement are enhanced, but intellectual property leakage risk increases
Solution Approach 1:
The patent introduces an intermediary transformation system that converts proprietary source code into a transformed version before submitting to third-party GAI. This intermediary layer protects the original code while enabling GAI processing, resolving the contradiction between productivity gain and IP protection.
Solution Approach 2:
The system creates a copy of the source code and transforms this copy into a protected format before submission to GAI. The transformation maintains code structure and logic necessary for GAI processing while removing or obfuscating proprietary identifiers, thus enabling productivity improvement without direct exposure of original IP.
2Object-affected harmful factors
If source code is transformed using one-way transform to protect proprietary information, then intellectual property protection is improved, but code readability and human understanding deteriorate
Solution Approach 1:
The transformation selectively applies protection only to specific elements that constitute proprietary information (such as variable names, function names, and comments) while preserving the overall code structure and logic. This localized approach maintains IP protection without completely sacrificing readability for developers who need to understand code flow.
Solution Approach 2:
The system transforms code parameters such as renaming identifiers to generic forms while maintaining their functional relationships. This changes the parameter representation to protect IP while preserving enough structure for developers to understand and work with the transformed code during the GAI interaction process.
3Adaptability or versatility
If proprietary source code is shared with third-party GAI, then access to advanced AI capabilities is improved, but data security and confidentiality worsen
Solution Approach 1:
The transformation system acts as a security intermediary between proprietary code and third-party GAI services. It enables access to advanced AI capabilities while maintaining data security by ensuring that even if the GAI provider accesses the transformed code, the original proprietary information cannot be reconstructed or leaked.
Solution Approach 2:
The system converts the potential harm of code exposure into a benefit by using the transformation process itself as a protection mechanism. The transformed code, while necessary for GAI processing, inherently protects the original IP, thus turning the act of sharing code into a secure interaction that benefits from the transformation barrier.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
There is disclosed herein a method of providing protected access to a third-party generative artificial intelligence (GAI) for software development including transforming a source code input (a transformed source code) via a one-way transform, wherein an enterprise has a proprietary interest in the source code input, and wherein the one-way transform is not internally reversible; providing the transformed source code to the third-party GAI, in association with a query for the third-party GAI, wherein the third-party GAI is controlled by a party other than the enterprise; receiving from the third-party GAI a response to the query; and presenting the response to a human user via a human interface device (HID).