Onto-DP: Constructing Neighborhoods for Differential Privacy on Ontological Databases
🤖 AI Summary
Traditional differential privacy models struggle to defend against attacks that exploit ontological inference rules, thereby failing to adequately protect sensitive information in semantic data. This work proposes Ontology-aware Differential Privacy (Onto-DP), the first mechanism to integrate ontological semantics into the differential privacy framework. By defining neighborhood relations that conform to ontological logic, Onto-DP constructs a privacy-preserving model that respects semantic constraints. The approach provides formal guarantees against inference-aware attacks, effectively addressing the security limitations of existing differential privacy mechanisms in semantic database environments.
Application Category
📝 Abstract
In this paper, we investigate how attackers can discover sensitive information embedded within databases by exploiting inference rules. We demonstrate the inadequacy of naively applied existing state of the art differential privacy (DP) models in safeguarding against such attacks. We introduce ontology aware differential privacy (Onto-DP), a novel extension of differential privacy paradigms built on top of any classical DP model by enriching it with semantic awareness. We show that this extension is a sufficient condition to adequately protect against attackers aware of inference rules.
Problem
Research questions and friction points this paper is trying to address.
differential privacy
ontological databases
inference attacks
semantic awareness
sensitive information
Innovation
Methods, ideas, or system contributions that make the work stand out.
Onto-DP
differential privacy
ontological databases
semantic awareness
inference attacks
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