Accountable Private Set Cardinality for Distributed Measurement

TL;DR

This paper presents cryptographic protocols for securely computing set cardinalities in distributed systems, enabling privacy-preserving statistics gathering with accountability and differential privacy guarantees.

Contribution

It introduces private set-cardinality protocols that are secure, efficient, and accountable, suitable for large-scale distributed measurement tasks.

Findings

Protocols operate with low computational overhead

Can count tens of thousands of observations within hours

Ensures privacy and accountability in distributed measurements

Abstract

We introduce cryptographic protocols for securely and efficiently computing the cardinality of set union and set intersection. Our private set-cardinality protocols (PSC) are designed for the setting in which a large set of parties in a distributed system makes observations, and a small set of parties with more resources and higher reliability aggregates the observations. PSC allows for secure and useful statistics gathering in privacy-preserving distributed systems. For example, it allows operators of anonymity networks such as Tor to securely answer the questions: "How many unique users are using the network?" and "How many hidden services are being accessed?". We prove the correctness and security of PSC in the Universal Composability framework against an active adversary that compromises all but one of the aggregating parties. Although successful output cannot be guaranteed in this setting, PSC either succeeds or terminates with an abort, and we furthermore make the adversary accountable for causing an abort by blaming at least one malicious party. We also show that PSC prevents adaptive corruption of the data parties from revealing past observations, which prevents them from being victims of targeted compromise, and we ensure safe measurements by making outputs differentially private. We present a proof-of-concept implementation of PSC and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. It can count tens of thousands of unique observations from tens to hundreds of data-collecting parties while completing within hours. PSC is thus suitable for daily measurements in a distributed system.