Machine-Compiled Model Weights with Authentication Gates for Edge Protection
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Solution Overview
Problem
Existing security measures for protecting model weights in machine-learning models deployed on edge devices are inadequate, as they can be easily stolen or recreated by attackers, posing financial and security risks.
Innovation Solution
Storing model weights as machine-compiled code and embedding a security gate layer within the machine-learning model that only allows inputs with valid authentication tokens, preventing direct access and indirect recreation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard encryption is used to protect models, then model protection during storage is improved, but model weights can still be easily accessed from memory location or buffer when decrypted for inferencing
Solution Approach 1:
The patent introduces an intermediary mechanism (security gate layer with authentication token verification) between the encrypted model storage and the model weights memory access. This intermediary controls and restricts access to model weights during inferencing, preventing direct unauthorized access even when the model is decrypted and loaded into memory.
2Speed
If model weights are stored in edge devices for on-device inferencing, then inference speed is improved, but developers lose control over the model leading to financial and security risks
Solution Approach 1:
The patent applies preliminary action by embedding authentication mechanisms and security gates into the model structure before deployment to edge devices. The model is pre-configured with security layers that verify authentication tokens and control access to weights, ensuring developer control is maintained even when the model operates independently on untrusted edge devices.
3Adaptability or versatility
If model weights are made accessible for inferencing, then model functionality is improved, but attackers can recreate the model by iteratively training another model using outputs
Solution Approach 1:
The patent implements preliminary anti-action by introducing a security gate layer that actively prevents model recreation attacks before they can succeed. The authentication token verification and controlled weight access mechanisms proactively block attempts to iteratively train or recreate the model, countering the threat before it can compromise the intellectual property.
Data Source
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AI summary
Proposed concepts thus aim to provide schemes, solutions, concepts, designs, methods and systems pertaining to protecting model weights. In particular, embodiments aim to provide a system for protecting model weights by having the model weights stored as machine-compiled code in a machine-learning model and then further embedding a security gate layer inside the machine-learning model which only passes through inputs with valid authentication tokens. In other words, it is proposed that by embedding a security gate layer within the machine-learning model itself, the gate layer can be less easily bypassed and model weights, stored as machine-compiled code, can be less easily indirectly or directly stolen.