DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming
CoRR(2023)
摘要
Streaming data, crucial for applications like crowdsourcing analytics,
behavior studies, and real-time monitoring, faces significant privacy risks due
to the large and diverse data linked to individuals. In particular, recent
efforts to release data streams, using the rigorous privacy notion of
differential privacy (DP), have encountered issues with unbounded privacy
leakage. This challenge limits their applicability to only a finite number of
time slots (''finite data stream'') or relaxation to protecting the events
(''event or $w$-event DP'') rather than all the records of users. A persistent
challenge is managing the sensitivity of outputs to inputs in situations where
users contribute many activities and data distributions evolve over time. In
this paper, we present a novel technique for Differentially Private data
streaming over Infinite disclosure (DPI) that effectively bounds the total
privacy leakage of each user in infinite data streams while enabling accurate
data collection and analysis. Furthermore, we also maximize the accuracy of DPI
via a novel boosting mechanism. Finally, extensive experiments across various
streaming applications and real datasets (e.g., COVID-19, Network Traffic, and
USDA Production), show that DPI maintains high utility for infinite data
streams in diverse settings. Code for DPI is available at
https://github.com/ShuyaFeng/DPI.
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