Encrypted 3D Object File Analysis for Printability Verification
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Solution Overview
Problem
Existing additive manufacturing technologies face challenges in ensuring the printability of complex designs, compatibility with post-printing processes, and interface with other parts while maintaining design confidentiality and privacy, especially when using untrusted service providers.
Innovation Solution
Implementing homomorphic encryption to perform privacy-preserving computations on 3D object models using a Minkowski sum protocol, allowing secure analysis of 3D object files without revealing proprietary information to service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 3D object models are sent to service providers for analysis and processing, then manufacturing quality and printability verification are improved, but intellectual property privacy and design confidentiality are compromised
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that enables the service provider to analyze and verify 3D object models without directly accessing the unencrypted design data. The encryption scheme acts as a mediator that preserves intellectual property confidentiality while still allowing necessary manufacturing verification processes to occur on the encrypted data.
Solution Approach 2:
The patent replaces the traditional mechanical approach of sending unencrypted files with a cryptographic system. Instead of physically securing the data transfer process, the system substitutes encryption mathematics to protect the 3D object models, allowing remote processing while maintaining security through mathematical transformations rather than physical security measures.
2Adaptability or versatility
If proprietary 3D designs are shared with untrusted service providers, then manufacturing capabilities are enhanced, but security and trust risks increase
Solution Approach 1:
Homomorphic encryption serves as a trusted intermediary that eliminates the need for direct trust between the design owner and service provider. The encryption system mediates the interaction by ensuring that the service provider can perform necessary manufacturing analyses on encrypted data without being able to view or misuse the proprietary designs, thus enhancing security reliability.
Solution Approach 2:
The patent creates encrypted copies of the 3D object models that contain all necessary manufacturing information but are protected from unauthorized viewing. These cryptographic copies allow the service provider to work with complete design data for manufacturing purposes while the original proprietary information remains securely protected, enabling capability expansion without trust risks.
3Loss of information
If complex 3D object models are processed locally, then intellectual property privacy is maintained, but computational resources and processing time are insufficient
Solution Approach 1:
Homomorphic encryption acts as an enabling intermediary that bridges the gap between local privacy preservation and remote processing power. It allows the system to leverage cloud-based computational resources for complex 3D model analysis while maintaining the same level of privacy protection as local processing, thus dramatically improving productivity without sacrificing confidentiality.
Solution Approach 2:
The patent moves the processing capability to a different dimension by utilizing remote cloud computing resources instead of being constrained by local hardware limitations. The homomorphic encryption enables this dimensional shift by allowing computations to occur in the encrypted domain on remote systems, providing access to powerful processing capabilities while maintaining the privacy benefits of local processing.
4Loss of information
If encrypted computations are performed on 3D object models, then privacy is maintained, but computational complexity and processing overhead increase
Solution Approach 1:
The patent replaces complex cryptographic operations with optimized mathematical transformations that can be performed efficiently on encrypted 3D data. By substituting traditional encryption-decryption cycles with homomorphic operations that work directly on encrypted data, the system reduces computational overhead while maintaining strong privacy protection throughout the processing pipeline.
Data Source
AI summary
The present disclosure provides techniques for finding non-printable features in a three-dimensional (3D) printable object. An example method includes receiving a first encrypted file and a request to process the first encrypted file to identify non-printable features of a 3D printable object, wherein the first encrypted file is an encrypted complement of an object file that comprises a specification of the 3D printable object. The method also includes computing a first encrypted Minkowski sum of the first encrypted file and an encrypted minimum feature file to generate an encrypted intermediate file and sending the encrypted intermediate file. The method also includes receiving a second encrypted file that comprises an encrypted complement of the encrypted intermediate file and computing a second encrypted Minkowski sum of the second encrypted file and the encrypted minimum feature file to generate an encrypted result file that describes non-printable features of the 3D printable object.