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.

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