Name
Abstract | ||
|---|---|---|
In previous work, we have investigated privacy-preserving publishing of Description Logic (DL) ontologies in a setting where the knowledge about individuals to be published is an EL instance store, and both the privacy policy and the possible background knowledge of an attacker are represented by concepts of the DL EL. We have introduced the notions of compliance of a concept with a policy and of safety of a concept for a policy, and have shown how, in the context mentioned above, optimal compliant (safe) generalizations of a given EL concept can be computed. In the present paper, we consider a modified setting where we assume that the background knowledge of the attacker is given by a DL different from the one in which the knowledge to be published and the safety policies are formulated. In particular, we investigate the situations where the attacker's knowledge is given by an FL0 or an FLE concept. In both cases, we show how optimal safe generalizations can be computed. Whereas the complexity of this computation is the same (ExpTime) as in our previous results for the case of FL0, it turns out to be actually lower (polynomial) for the more expressive DL FLE. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30179-8_7 | ADVANCES IN ARTIFICIAL INTELLIGENCE, KI 2019 |
DocType | Volume | ISSN |
Conference | 11793 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franz Baader | 1 | 8123 | 646.64 |
| Adrian Nuradiansyah | 2 | 0 | 1.69 |