Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss.
However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics.
In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance.
A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts the pDP of other instances, adding complexity and resilience to straightforward solutions.
To tackle this problem, we introduce an instance-wise Laplace mechanism algorithm via deep reinforcement learning and validate its ability to better preserve data statistics on a real dataset, compared to the original Laplace mechanism.

Sehyun Ryu

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