Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms
This work addresses privacy concerns for data disclosure in machine learning, but it is incremental as it builds on existing information-theoretic approaches with specific empirical improvements.
The paper tackles the problem of disclosing training data while protecting sensitive labels by developing a privacy-preserving mechanism that perturbs data vectors to balance privacy and utility, using maximal information leakage as a privacy metric. It shows that the optimal solution matches cases where utility is measured by adversary error probability and demonstrates on MNIST and FERG datasets that the framework achieves equivalent or better privacy than previous mutual information-based methods.
It is often necessary to disclose training data to the public domain, while protecting privacy of certain sensitive labels. We use information theoretic measures to develop such privacy preserving data disclosure mechanisms. Our mechanism involves perturbing the data vectors in a manner that strikes a balance in the privacy-utility trade-off. We use maximal information leakage between the output data vector and the confidential label as our privacy metric. We first study the theoretical Bernoulli-Gaussian model and study the privacy-utility trade-off when only the mean of the Gaussian distributions can be perturbed. We show that the optimal solution is the same as the case when the utility is measured using probability of error at the adversary. We then consider an application of this framework to a data driven setting and provide an empirical approximation to the Sibson mutual information. By performing experiments on the MNIST and FERG data-sets, we show that our proposed framework achieves equivalent or better privacy than previous methods based on mutual information.