Details
| Original language | English |
|---|---|
| Title of host publication | Database and Expert Systems Applications |
| Subtitle of host publication | 30th International Conference, DEXA 2019, Linz, Austria, August 26–29, 2019, Proceedings |
| Editors | Sven Hartmann, Josef Küng, Sharma Chakravarthy, Gabriele Anderst-Kotsis, A Min Tjoa, Ismail Khalil |
| Pages | 205-214 |
| Number of pages | 10 |
| Volume | I |
| Edition | 1. |
| ISBN (electronic) | 978-3-030-27615-7 |
| Publication status | Published - 3 Aug 2019 |
| Event | 30th International Conference on Database and Expert Systems Applications, DEXA 2019 - Linz, Austria Duration: 26 Aug 2019 → 29 Aug 2019 |
Publication series
| Name | Lecture Notes in Computer Science (LNISA) |
|---|---|
| Volume | 11706 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Database and Expert Systems Applications : 30th International Conference, DEXA 2019, Linz, Austria, August 26–29, 2019, Proceedings. ed. / Sven Hartmann; Josef Küng; Sharma Chakravarthy; Gabriele Anderst-Kotsis; A Min Tjoa; Ismail Khalil. Vol. I 1. ed. 2019. p. 205-214 (Lecture Notes in Computer Science (LNISA); Vol. 11706).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - PURE
T2 - 30th International Conference on Database and Expert Systems Applications, DEXA 2019
AU - Goncalves, Marlene
AU - Vidal, Maria Esther
AU - Endris, Kemele M.
N1 - Funding information: This work has been partially supported by the EU H2020 RIA funded project iASiS with grant agreement No. 727658.
PY - 2019/8/3
Y1 - 2019/8/3
N2 - Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.
AB - Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.
UR - http://www.scopus.com/inward/record.url?scp=85077125619&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-27615-7_15
DO - 10.1007/978-3-030-27615-7_15
M3 - Conference contribution
AN - SCOPUS:85077125619
SN - 978-3-030-27614-0
VL - I
T3 - Lecture Notes in Computer Science (LNISA)
SP - 205
EP - 214
BT - Database and Expert Systems Applications
A2 - Hartmann, Sven
A2 - Küng, Josef
A2 - Chakravarthy, Sharma
A2 - Anderst-Kotsis, Gabriele
A2 - Tjoa, A Min
A2 - Khalil, Ismail
Y2 - 26 August 2019 through 29 August 2019
ER -