Personalized Privacy-Preserving Data Aggregation for Histogram Estimation

Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose an aggregation scheme for histogram estimation, wherein participants can publish their data at personalized differential-privacy levels. The aggregator also benefits from potential wider engagement or more honest data. Specially, since privacy levels under personalized privacy policy are sensitive information for participants, our scheme permits participants to keep their privacy levels secret even from the aggregator. We also show how to further optimize the estimation accuracy under given privacy levels by choosing specific randomization strategies.