DPlanner: A Privacy Budgeting System for Utility

• Published in: IEEE transactions on information forensics and security 2023-01, Vol.18, p.1-1
• Main Authors: Li, Weiting, Xiang, Liyao, Guo, Bin, Li, Zhetao, Wang, Xinbing
• Format: Article
• Language: English
• Subjects: Budgeting; Budgets; Data models; Data privacy; Differential privacy; Machine learning; Privacy; Queries; Schedules; Scheduling; Training data
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2022.3231786

Differential privacy has been deployed to machine learning platforms to preserve the privacy of data in use. A long neglected but important fact is that data privacy is a non-replenishable resource and should be carefully scheduled to maximize its utility gain. In this work, we propose a new privacy budgeting system - DPlanner, which estimates data blocks' importance to queries and assigns fractional privacy budget to those data blocks contributing most to a query. The scheduler is novelly designed to include two-fold randomness, which satisfies differential privacy with tight budgets, at the same time guarantees the expected utility in the worst-case query sequence when queries arrive in an online fashion. Experiments in a variety of machine learning settings have shown that our DPlanner outperforms the state-of-the-art schedulers by serving at least 25% more queries, or reducing the total privacy consumption by over 50%.