Modelling and Quantifying Membership Information Leakage in Machine Learning

29 Jan 2020Farhad FarokhiMohamed Ali Kaafar

Machine learning models have been shown to be vulnerable to membership inference attacks, i.e., inferring whether individuals' data have been used for training models. The lack of understanding about factors contributing success of these attacks motivates the need for modelling membership information leakage using information theory and for investigating properties of machine learning models and training algorithms that can reduce membership information leakage... (read more)

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