Locally Differentially Private Applications with Longitudinal Data
DOI:
https://doi.org/10.5753/jidm.2026.5735Keywords:
Local Differential Privacy, Longitudinal Data, Frequency Oracle ProtocolsAbstract
Local differential privacy (LDP) was developed as a version of differential privacy (DP) that does not require a trusted curator or server. Frequency oracles, a class of LDP protocols for frequency estimation, function as the building blocks for diverse applications with LDP guarantees developed for tackling specific tasks such as answering range queries, and frequent item and itemset mining. However, these applications often build on frequency oracles with no adjustments for longitudinal data, and therefore can not provide LDP. In this paper, we investigate the practical effectiveness of state-of-the-art frequency oracle (FO) protocols designed for longitudinal data in various data analysis tasks. Specifically, we implement these protocols to perform three key tasks: answering range queries, identifying frequent items, and detecting frequent itemsets. Additionally, we incorporate post-processing techniques to enhance utility and improve overall performance. Our experimental evaluation includes four real-world datasets from diverse domains, allowing us to systematically measure and compare the utility of longitudinal LDP protocols.
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