A Review of Deep Learning Security and Privacy Defensive Techniques

Table 2

Comparison of countermeasure techniques of Deep Learning.

Countermeasure methods	Advantages	Disadvantages
Adversarial training [94]	Very easy to understand and implement	It depends upon the sample size in the training phase
	Scalable and have the ability to handle the complex dataset
Defense distillation [80]	Sample and have the defense ability	Difficult to converge and high complexity
Ensemble method [95]	Model-independent, good generalization	Do not rebut the training data and computation overhead
Differential Privacy [96]	Preserves the privacy of training and learning data	It also affects legitimate data and model-independent
	Low overhead, low complexity
Homomorphic Encryption [97]	Maintains security and privacy of data and simple	It increases the data size and extensive computation overhead