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New privacy framework 'predictability' complements differential privacy

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a new privacy framework called "privacy via predictability" that offers a more fine-grained approach than traditional differential privacy (DP). This new method accounts for an attacker's specific knowledge, a compromised portion of the dataset, and the types of queries being made. Predictability measures privacy leakage by assessing how much an attacker's ability to predict sensitive information improves after observing an algorithm's output, beyond what's already known from compromised data. The framework is complementary to DP and can be used alongside it for enhanced privacy control. AI

IMPACT This new privacy framework could lead to more nuanced and effective data protection methods in AI systems.

RANK_REASON The cluster contains an academic paper detailing a new privacy framework.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New privacy framework 'predictability' complements differential privacy

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Linda Lu, Karthik Sridharan ·

    Predictability as a Fine-Grained Measure for Privacy

    arXiv:2606.20546v1 Announce Type: new Abstract: 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 predicta…

  2. arXiv cs.LG TIER_1 English(EN) · Karthik Sridharan ·

    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…

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