Private Stream Aggregation Revisited
This work addresses privacy-preserving data aggregation for distributed networks, offering incremental improvements in security models and efficiency.
The paper tackles the problem of private statistical analysis in distributed settings by revisiting Private Stream Aggregation schemes, showing they can be built on key-homomorphic weak pseudo-random functions for standard model security and introducing a Skellam distribution-based perturbation mechanism that improves performance over prior solutions.
In this work, we investigate the problem of private statistical analysis in the distributed and semi-honest setting. In particular, we study properties of Private Stream Aggregation schemes, first introduced by Shi et al. \cite{2}. These are computationally secure protocols for the aggregation of data in a network and have a very small communication cost. We show that such schemes can be built upon any key-homomorphic \textit{weak} pseudo-random function. Thus, in contrast to the aforementioned work, our security definition can be achieved in the \textit{standard model}. In addition, we give a computationally efficient instantiation of this protocol based on the Decisional Diffie-Hellman problem. Moreover, we show that every mechanism which preserves $(ε,δ)$-differential privacy provides \textit{computational} $(ε,δ)$-differential privacy when it is executed through a Private Stream Aggregation scheme. Finally, we introduce a novel perturbation mechanism based on the \textit{Skellam distribution} that is suited for the distributed setting, and compare its performances with those of previous solutions.