Secure Machine Learning Model Updates Through Encrypted Packet Mediation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack secure methods for updating machine learning models and fail to rate models for accuracy and request necessary updates, leading to potential security vulnerabilities and inefficiencies.
Innovation Solution
A system comprising a processing device and encryption devices with encryption and communication units that encrypt and decrypt packets using connectionless headers to facilitate secure updating of machine learning models, ensuring secure communication across untrusted networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing technologies are used for updating machine learning models, then the updating process is simple, but security vulnerabilities arise due to lack of encryption and secure communication protocols
Solution Approach 1:
The patent introduces encryption devices as intermediary components between the processing device and the machine learning model update source. These encryption devices establish secure communication channels by encrypting data packets before transmission and decrypting them upon receipt, thereby mediating the update process to ensure security without requiring the processing device itself to implement complex cryptographic protocols
Solution Approach 2:
The system is segmented into distinct functional components: processing devices that generate update requests, encryption devices that secure the communication, and model sources that provide updates. This segmentation allows each component to specialize in its function, with encryption devices handling security complexity while processing devices focus on model operations, thus improving security without overly complicating the overall system architecture
2Reliability
If secure encryption protocols are implemented for model updates, then security is improved, but communication overhead and processing time increase
Solution Approach 1:
Encryption devices establish secure communication channels and exchange cryptographic keys in advance before actual model update transmissions occur. This preliminary setup of security infrastructure allows subsequent update communications to proceed efficiently without repeated key exchange and channel establishment overhead, reducing the time penalty associated with secure protocols
3Measurement precision
If machine learning models are updated frequently to maintain accuracy, then model performance is improved, but security exposure increases due to more update transactions
Solution Approach 1:
Encryption devices serve as persistent secure intermediaries that maintain established cryptographic channels for frequent model update transactions. By reusing these pre-established secure channels across multiple update operations, the system can frequently update models to maintain accuracy while avoiding the security risks associated with repeated channel establishment and key exchange operations
Data Source
AI summary
A system for facilitating secure updating of a machine learning model. The system includes a processing device and a first encryption device. The processing device generates a request and updates the machine learning model using an update. The first encryption device includes a first encryption unit and a first communication unit. The first encryption unit encrypts a native packet corresponding to the request and adds a connectionless header forming a first egressing connectionless datagram, decrypts a second encrypted native packet of an ingressing connectionless datagram to obtain the update. The first communication unit adds a complex header to the first egressing connectionless datagram for forming a first packet for delivery to a second encryption device, receives a second packet comprising the second encrypted native packet and a complex header from the second encryption device, removes the complex header and adds a connectionless header for forming the ingressing connectionless datagram.