UCLA

We present a framework for privacy-preserving streaming algorithms which combine the memory-efficiency of streaming algorithms with strong privacy guarantees.These algorithms enable some number of servers to compute aggregate statistics efficiently on large quantities of user data without learning the user's inputs. While there exists limited prior work that fits within our model, our work is the first to formally define a general framework, interpret existing methods within this general framework, and develop new tools broadly applicable to this model. To highlight our model, we designed and implemented a new privacy-preserving streaming algorithm to compute heavy hitters, which are the most frequent elements in a data stream. We provide a performance comparison between our system and Poplar, the only other private statistics algorithm which supports heavy hitters. We benchmarked ours and Poplar's systems and provided direct performance comparisons within the same hardware platform. Of note, Poplar requires linear space compared to our poly-logarithmic space, meaning our system is the first to compute heavy hitters within the privacy-preserving streaming model. A small memory footprint allows our algorithm (among other benefits) to run efficiently on very large input sizes without running out of memory or crashing.