The proposed α-leakage measures, based on Rényi divergence and Sibson mutual information, offer a robust framework for quantifying information leakage in practical privacy-preserving systems. By utilizing these measures, system designers can effectively assess the amount of information that an adversary can obtain about sensitive data. This quantification of information leakage is crucial in guiding the design of optimal privacy-utility trade-offs in such systems.
To apply these α-leakage measures in practice, system designers can first analyze the specific characteristics of the system, including the data types, communication channels, and potential adversaries. By understanding these factors, they can tailor the α-leakage measures to suit the system's requirements accurately.
Once the α-leakage measures are integrated into the system, they can be used to evaluate the effectiveness of existing privacy mechanisms and identify potential vulnerabilities. By quantifying the information leakage, system designers can make informed decisions about enhancing privacy protections while balancing utility considerations. This iterative process allows for the optimization of privacy-preserving systems to achieve the desired level of privacy without compromising utility.