Preserving Confidentiality of Deep Learning Model Architectures during Performance Optimizations

Core Concepts

PROTEUS, a novel mechanism that enables model optimization by an independent party while preserving the confidentiality of the model architecture.

Abstract

The paper presents PROTEUS, a mechanism that aims to preserve the confidentiality of deep learning (DL) model architectures during performance optimizations by an independent party.
Key highlights:

DL model development and optimization are typically done by different parties, requiring the model developers to expose the model architecture, which is an important intellectual property.
PROTEUS obfuscates the protected model by partitioning its computational graph into subgraphs and concealing each subgraph within a large pool of generated realistic subgraphs.
This approach effectively hides the model as one alternative among up to 10^32 possible model architectures, making it infeasible for an adversary to recover the original model.
PROTEUS retains the ability of the optimizer to provide significant speedups via graph-level optimization, with an average slowdown within 10% of the maximum attainable.
The paper evaluates PROTEUS on a range of DNN models and demonstrates its effectiveness in preserving confidentiality without compromising performance optimization opportunities.

Stats

OpenAI reports a daily cost of $700K to run ChatGPT.
TVM can provide up to 3.8× speedup on model inference.

Quotes

"PROTEUS effectively hides the model as one alternative among up to 10^32 possible model architectures, and is resilient against attacks with a learning-based adversary."
"To our knowledge, PROTEUS is the first work that tackles the challenge of model confidentiality during performance optimization."

Key Insights Distilled From

Proteus: Preserving Model Confidentiality during Graph Optimizations

by Yubo Gao,Mar... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12512.pdf

Deeper Inquiries

How can PROTEUS be extended to protect the confidentiality of the optimization techniques and compilers themselves

To extend PROTEUS to protect the confidentiality of the optimization techniques and compilers themselves, we can implement a similar obfuscation mechanism for the optimization techniques and compilers. This would involve partitioning the optimization techniques and compiler algorithms into smaller components or modules and generating sentinel versions of these components. By doing so, the actual optimization techniques and compiler algorithms would be hidden within a pool of artificially generated versions, making it difficult for adversaries to identify the original techniques and algorithms. Additionally, similar to how PROTEUS preserves the confidentiality of the model architecture during optimization, this extended version could ensure that the optimization techniques and compilers remain secure and protected from unauthorized access or reverse engineering.

What are the potential limitations of PROTEUS in scenarios where the protected model is structurally very similar to commonly-used popular DL models

One potential limitation of PROTEUS in scenarios where the protected model is structurally very similar to commonly-used popular DL models is the effectiveness of the obfuscation mechanism. If the protected model closely resembles widely known DL models, the generation of sentinel subgraphs may not provide enough variability to sufficiently obfuscate the original model. In such cases, the sentinel subgraphs may not be diverse enough to effectively hide the protected model, potentially making it easier for adversaries to identify the original model. This limitation could compromise the confidentiality of the model architecture in scenarios where the protected model shares significant similarities with popular DL models.

How can PROTEUS be adapted to handle dynamic changes in the model architecture during the optimization process

To adapt PROTEUS to handle dynamic changes in the model architecture during the optimization process, the obfuscation mechanism can be designed to be flexible and adaptive. This could involve incorporating dynamic graph partitioning algorithms that can adjust the partitioning of the computational graph based on changes in the model architecture. Additionally, the generation of sentinel subgraphs could be optimized to quickly adapt to modifications in the model structure, ensuring that the obfuscation remains effective even as the model architecture evolves. By implementing mechanisms for real-time updates and adjustments in the obfuscation process, PROTEUS can effectively handle dynamic changes in the model architecture during optimization.