The content discusses the challenges of maintaining privacy over time, focusing on the private counter problem and its implications. It introduces new lower bounds that extend to online threshold queries, highlighting separations between private and non-private online learning models.
The authors use Ramsey theory to prove their lower bounds, showcasing the complexity of ensuring differential privacy in evolving data scenarios. The content emphasizes the importance of understanding error dependencies on total time steps and event numbers.
Through detailed proofs and examples, the authors demonstrate the intricacies of maintaining privacy in continual observation settings, shedding light on the limitations of current algorithms.
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