论文标题
数据集混淆：它对边缘机器学习的应用程序和影响
Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning
论文作者
论文摘要
通过添加随机噪声来保护数据集以保护训练数据集中敏感样本的隐私对于防止数据泄漏到边缘应用程序不受信任的各方泄漏而产生的敏感样本隐私至关重要。我们进行全面的实验，以研究数据集混淆如何影响所得的模型权重 - 就模型的准确性，Frobenius-Norm（F-Norm）基于基于的模型距离和数据隐私级别而言，并与拟议的隐私性，实用性，实用性和区分（PUD） - Triangle-Triangle图表概率讨论了潜在的应用程序。我们的实验基于独立和相同分布（IID）和非IID设置下的流行MNIST和CIFAR-10数据集。重大结果包括模型准确性和隐私水平之间的权衡以及模型差异和隐私水平之间的权衡。结果表明，在边缘计算中训练外包的广泛应用前景，并防止边缘设备中联合学习的攻击。
Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge applications. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights - in terms of the model accuracy, Frobenius-norm (F-norm)-based model distance, and level of data privacy - and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a trade-off between the model accuracy and privacy level and a trade-off between the model difference and privacy level. The results indicate broad application prospects for training outsourcing in edge computing and guarding against attacks in Federated Learning among edge devices.
