# Attacking and Defending Machine Learning Applications of Public Cloud

arXiv: https://arxiv.org/abs/2008.02076 · HTML: https://mlsanity.com/paper/2008.02076/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Benchmarking Robustness to Adversarial Image Obfuscations](https://mlsanity.com/paper/2301.12993/) (2023)
- [OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS](https://mlsanity.com/paper/2105.11593/) (2021)
- [Data-Free Hard-Label Robustness Stealing Attack](https://mlsanity.com/paper/2312.05924/) (2023)

## Abstract (arXiv)

Adversarial attack breaks the boundaries of traditional security defense. For adversarial attack and the characteristics of cloud services, we propose Security Development Lifecycle for Machine Learning applications, e.g., SDL for ML. The SDL for ML helps developers build more secure software by reducing the number and severity of vulnerabilities in ML-as-a-service, while reducing development cost.