The paper presents a method to achieve differentially private prediction with reduced dependence on the number of queries, improving efficiency in streaming settings.
This research focuses on making predictions while ensuring privacy, based on a technique called differential privacy. Typically, the privacy cost increases with the number of predictions made, but the authors have developed a way to keep this cost low, even as the number of predictions grows. This is particularly useful when predictions are made in a sequence, such as in a live data stream. They achieve this by reducing the number of labeled examples needed to maintain privacy, making the process more efficient and practical.
