Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
This work addresses privacy-preserving data analysis for distributed systems, offering efficient protocols with theoretical guarantees, though it appears incremental as it builds on existing differential privacy models.
The paper tackles the problem of frequency estimation under privacy and communication constraints in distributed settings, achieving optimal error bounds (up to logarithmic factors) for both one-shot and streaming scenarios under multiparty differential privacy.
We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties. We consider two application scenarios: (1) one-shot, where the data is static and the aggregator conducts a one-time computation; and (2) streaming, where each party receives a stream of items over time and the aggregator continuously monitors the frequencies. We adopt the model of multiparty differential privacy (MDP), which is more general than local differential privacy (LDP) and (centralized) differential privacy. Our protocols achieve optimality (up to logarithmic factors) permissible by the more stringent of the two constraints. In particular, when specialized to the $\varepsilon$-LDP model, our protocol achieves an error of $\sqrt{k}/(e^{Θ(\varepsilon)}-1)$ using $O(k\max\{ \varepsilon, \frac{1}{\varepsilon} \})$ bits of communication and $O(k \log u)$ bits of public randomness, where $u$ is the size of the domain.