Name
Abstract | ||
|---|---|---|
Machine learning models have been widely deployed in many real-world tasks. When a non-expert data holder wants to use a third-party machine learning service for model training, it is critical to preserve the confidentiality of the training data. In this paper, we for the first time explore the potential privacy leakage in a scenario that a malicious ML provider offers data holder customized training code including model compression which is essential in practical deployment The provider is unable to access the training process hosted by the secured third party, but could inquire models when they are released in public. As a result, adversary can extract sensitive training data with high quality even from these deeply compressed models that are tailored for resource-limited devices. Our investigation shows that existing compressions like quantization, can serve as a defense against such an attack, by degrading the model accuracy and memorized data quality simultaneously. To overcome this defense, we take an initial attempt to design a simple but stealthy quantized correlation encoding attack flow from an adversary perspective. Three integrated components-data pre-processing, layer-wise data-weight correlation regularization, data-aware quantization, are developed accordingly. Extensive experimental results show that our framework can preserve the evasiveness and effectiveness of stealing data from compressed models. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DAC18072.2020.9218633 | 2020 57th ACM/IEEE Design Automation Conference (DAC) |
DocType | ISSN | ISBN |
Conference | 0738-100X | 978-1-7281-1085-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||