Основні поняття
PROTEUS, a novel mechanism that enables model optimization by an independent party while preserving the confidentiality of the model architecture.
Анотація
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.
Статистика
OpenAI reports a daily cost of $700K to run ChatGPT.
TVM can provide up to 3.8× speedup on model inference.
Цитати
"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."