Safe Reasoning Over Ontologies

TL;DR

This paper addresses the challenge of ensuring ontologies do not infer sensitive information, proposing methods to test safeness and optimize ontologies for maximum useful data without compromising privacy.

Contribution

It introduces a framework for testing ontology safeness and optimizing ontologies to balance information utility and privacy protection.

Findings

Proposed a method to test if an ontology can infer sensitive facts.

Developed an approach to optimize ontologies for maximum useful information without revealing sensitive data.

Addressed the balance between inference power and privacy in ontology management.

Abstract

As ontologies proliferate and automatic reasoners become more powerful, the problem of protecting sensitive information becomes more serious. In particular, as facts can be inferred from other facts, it becomes increasingly likely that information included in an ontology, while not itself deemed sensitive, may be able to be used to infer other sensitive information. We first consider the problem of testing an ontology for safeness defined as its not being able to be used to derive any sensitive facts using a given collection of inference rules. We then consider the problem of optimizing an ontology based on the criterion of making as much useful information as possible available without revealing any sensitive facts.