@Article{jihpp.2021.027871,
AUTHOR = {Jumana Alsubhi, Abdulrahman Gharawi, Mohammad Alahmadi},
TITLE = {A Performance Study of Membership Inference Attacks on Different Machine  Learning Algorithms},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {193--200},
URL = {http://www.techscience.com/jihpp/v3n4/47058},
ISSN = {2637-4226},
ABSTRACT = {Nowadays, machine learning (ML) algorithms cannot succeed without 
the availability of an enormous amount of training data. The data could contain 
sensitive information, which needs to be protected. Membership inference 
attacks attempt to find out whether a target data point is used to train a certain 
ML model, which results in security and privacy implications. The leakage of 
membership information can vary from one machine-learning algorithm to 
another. In this paper, we conduct an empirical study to explore the performance 
of membership inference attacks against three different machine learning 
algorithms, namely, K-nearest neighbors, random forest, support vector machine, 
and logistic regression using three datasets. Our experiments revealed the best 
machine learning model that can be more immune to privacy attacks. 
Additionally, we examined the effects of such attacks when varying the dataset 
size. Based on our observations for the experimental results, we propose a 
defense mechanism that is less prone to privacy attacks and demonstrate its 
effectiveness through an empirical evaluation.},
DOI = {10.32604/jihpp.2021.027871}
}