Predictability as a Fine-Grained Measure for Privacy
Differential privacy (DP) ensures rigorous individual-level privacy guarantees against even the most knowledgeable attackers, but its worst-case nature can impose a costly privacy-accuracy tradeoff. We introduce privacy via predictability, a fine-grained framework that explicitly incorporates the attacker's core knowledge, a compromised portion of the dataset generated by a stochastic process, and a specified family of queries. Predictability measures privacy leakage as the incremental gain in an attacker's ability to predict sensitive information about unknown individuals after observing the algorithm's output, beyond what can already be inferred from the compromised data. We show that p...
| Authors | Linda Lu, Karthik Sridharan |
|---|---|
| Primary category | cs.LG |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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