Anonymized Local Privacy
This work addresses privacy concerns in distributed on-demand networks, offering a novel approach to local privacy with potential applications in data sharing and analysis.
The paper tackles the problem of protecting privacy in distributed data aggregation by introducing Anonymized Local Privacy mechanisms, which use a three-value output and linear noise to achieve privacy before and after aggregation while preserving accuracy, as evaluated on a real dataset with scaling populations.
In this paper, we introduce the family of Anonymized Local Privacy mechanisms. These mechanisms have an output space of three values "Yes", "No", or "$\perp$" (not participating) and leverage the law of large numbers to generate linear noise in the number of data owners to protect privacy both before and after aggregation yet preserve accuracy. We describe the suitability in a distributed on-demand network and evaluate over a real dataset as we scale the population.