Frequency Estimation of Evolving Data Under Local Differential Privacy

TL;DR

This paper introduces LOLOHA, a local differential privacy protocol for evolving data that reduces privacy costs and maintains high utility by combining domain reduction and double randomization.

Contribution

LOLOHA is a novel LDP protocol that significantly decreases privacy budget consumption for longitudinal frequency estimation through local hashing and double randomization.

Findings

LOLOHA reduces privacy budget consumption by up to k/g times.

The protocol achieves utility comparable to state-of-the-art methods.

Theoretical and experimental results validate LOLOHA's effectiveness.

Abstract

Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential privacy (LDP) protocols, which ensure the privacy protection of all users even in the case of a breach or data misuse. Existing LDP data collection protocols such as Google's RAPPOR and Microsoft's dBitFlipPM can have longitudinal privacy linear to the domain size k, which is excessive for large domains, such as Internet domains. To solve this issue, in this paper we introduce a new LDP data collection protocol for longitudinal frequency monitoring named LOngitudinal LOcal HAshing (LOLOHA) with formal privacy guarantees. In addition, the privacy-utility trade-off of our protocol is only linear with respect to a reduced domain size $2\leq g \ll k$. LOLOHA combines a domain reduction approach via local hashing with double randomization to minimize the privacy leakage incurred by data updates. As demonstrated by our theoretical analysis as well as our experimental evaluation, LOLOHA achieves a utility competitive to current state-of-the-art protocols, while substantially minimizing the longitudinal privacy budget consumption by up to k/g orders of magnitude.