@Article{jihpp.2021.027385,
AUTHOR = {Mingting Liu, Xiaozhang Liu, Anli Yan, Xiulai Li, Gengquan Xie, Xin Tang},
TITLE = {An Explanatory Strategy for Reducing the Risk of Privacy Leaks},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {181--192},
URL = {http://www.techscience.com/jihpp/v3n4/47057},
ISSN = {2637-4226},
ABSTRACT = {As machine learning moves into high-risk and sensitive applications 
such as medical care, autonomous driving, and financial planning, how to 
interpret the predictions of the black-box model becomes the key to whether 
people can trust machine learning decisions. Interpretability relies on providing 
users with additional information or explanations to improve model transparency 
and help users understand model decisions. However, these information 
inevitably leads to the dataset or model into the risk of privacy leaks. We 
propose a strategy to reduce model privacy leakage for instance interpretability 
techniques. The following is the specific operation process. Firstly, the user 
inputs data into the model, and the model calculates the prediction confidence of 
the data provided by the user and gives the prediction results. Meanwhile, the 
model obtains the prediction confidence of the interpretation data set. Finally, the 
data with the smallest Euclidean distance between the confidence of the 
interpretation set and the prediction data as the explainable data. Experimental 
results show that The Euclidean distance between the confidence of 
interpretation data and the confidence of prediction data provided by this method 
is very small, which shows that the model's prediction of interpreted data is very 
similar to the model's prediction of user data. Finally, we demonstrate the 
accuracy of the explanatory data. We measure the matching degree between the 
real label and the predicted label of the interpreted data and the applicability to 
the network model. The results show that the interpretation method has high 
accuracy and wide applicability.},
DOI = {10.32604/jihpp.2021.027385}
}