Private Computations on Streaming Data

ePrint Report: Private Computations on Streaming Data

Vladimir Braverman, Kevin Garbe, Eli Jaffe, Rafail Ostrovsky

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 …

algorithms compute data efficiency enable eprint report framework kevin large memory privacy private report servers statistics streaming user data vladimir