Lightweight Techniques for Private Heavy Hitters
Poplar provides a privacy-preserving method for identifying popular data items, which is significant for organizations like web-browser vendors who need to collect aggregate statistics without learning individual user data.
This paper introduces Poplar, a system designed to solve the private heavy-hitters problem, enabling servers to identify popular strings (e.g., web homepages) without compromising individual client privacy. It also addresses the private subset-histogram problem. Poplar achieves this with two data-collection servers, where each client sends only a single message per protocol run.
This paper presents Poplar, a new system for solving the private heavy-hitters problem. In this problem, there are many clients and a small set of data-collection servers. Each client holds a private bitstring. The servers want to recover the set of all popular strings, without learning anything else about any client's string. A web-browser vendor, for instance, can use Poplar to figure out which homepages are popular, without learning any user's homepage. We also consider the simpler private subset-histogram problem, in which the servers want to count how many clients hold strings in a particular set without revealing this set to the clients. Poplar uses two data-collection servers and, in a protocol run, each client send sends only a single message to the servers. Poplar protects client privacy against arbitrary misbehavior by one of the servers and our approach requires no public-key cryptography (except for secure channels), nor general-purpose multiparty computation. Instead, we rely on incremental distributed point functions, a new cryptographic tool that allows a client to succinctly secret-share the labels on the nodes of an exponentially large binary tree, provided that the tree has a single non-zero path. Along the way, we develop new general tools for providing malicious security in applications of distributed point functions.