TY - GEN
T1 - Dynamic and scalable enforcement of access control policies for big data
AU - Anisetti, Marco
AU - Ardagna, Claudio A.
AU - Braghin, Chiara
AU - Damiani, Ernesto
AU - Polimeno, Antongiacomo
AU - Balestrucci, Alessandro
N1 - Funding Information:
Some of thework reported here is supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 883286, project IMPETUS.
Funding Information:
Some of the work reported here is supported by funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883286, project “IMPETUS”.
Publisher Copyright:
© 2021 ACM.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - The conflict between the need of protecting and sharing data is hampering the spread of big data applications. Security and privacy assurance is required to protect data owners, while data access and sharing are fundamental to implement smart big data solutions. In this context, access control systems can assume a central role in balancing data protection and data sharing. However, existing access control solutions are not general and scalable enough to address the software and technological complexity of big data ecosystems, being unable to support such a dynamic and collaborative environment. In this paper, we propose an access control system that enforces access to data in a distributed, multi-party big data environment. It is based on data annotations and secure data transformations performed at ingestion time. We show the feasibility of our approach in the smart city domain using an Apache-based big data engine.
AB - The conflict between the need of protecting and sharing data is hampering the spread of big data applications. Security and privacy assurance is required to protect data owners, while data access and sharing are fundamental to implement smart big data solutions. In this context, access control systems can assume a central role in balancing data protection and data sharing. However, existing access control solutions are not general and scalable enough to address the software and technological complexity of big data ecosystems, being unable to support such a dynamic and collaborative environment. In this paper, we propose an access control system that enforces access to data in a distributed, multi-party big data environment. It is based on data annotations and secure data transformations performed at ingestion time. We show the feasibility of our approach in the smart city domain using an Apache-based big data engine.
KW - Access control
KW - Big data
KW - Data ingestion
KW - Data transformation
UR - http://www.scopus.com/inward/record.url?scp=85121663343&partnerID=8YFLogxK
U2 - 10.1145/3444757.3485107
DO - 10.1145/3444757.3485107
M3 - Conference contribution
AN - SCOPUS:85121663343
T3 - ACM International Conference Proceeding Series
SP - 71
EP - 78
BT - Proceedings of 2021 13th International Conference on Management of Digital EcoSystems, MEDES 2021
T2 - 13th International Conference on Management of Digital EcoSystems, MEDES 2021
Y2 - 1 November 2021 through 3 November 2021
ER -
