Dynamic and Scalable Enforcement of Access Control Policies for Big Data

Abstract

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. CCS CONCEPTS• Security and privacy → Database and storage security; • Computer systems organization → Distributed architectures; • Information systems → Extraction, transformation and loading.

Revisiting Trust Management in the Data Economy: A Road Map

“…revolution, with the European strategy for data 4 and other initiatives, such as the Data Governance Act 5 and the Common European Data spaces. 6 The potential economic impact is huge, enabling companies to fully capitalize on the latent value embedded within their data.…”

“…However, data pooling and sharing lose value without robust data protection measures. A considerable research and development effort has been dedicated to provide advanced data governance solutions for (balancing) data quality and protection [4]. While significant, this effort is inadequate in today's data economy for several reasons: i) (unknown) data providers and consumers come and go; ii) each party has its own set of potentially conflicting requirements that must be enforced on collected and exchanged data, analytics results, metadata, primary and secondary usage of data, recursively.…”

Real-Time Anonymization of Sensitive Personal Data Using a Service-Based Architecture

“…• R1: Right to access the data must be evaluated before data analytics takes place. • R2: As federations within big data ecosystem must be considered, authentication should be managed by a separate and integrated module [1] • R3: data must be properly protected and shared only to authorized users and for authorized operations. Access control must protect data during their entire life cycle • R4: Fine-grained access control must be supported, dealing with both structured and unstructured data.…”

Smart City Development: Data Sharing vs. Data Protection Legislations

“…However, it must be noted that these technical solutions do not provide an inclusive solution to how CDS can be achieved, such as by providing a practical guideline covering the challenges and requirements. Technical solutions include a solution by Anisetti et al (2021), where they proposed an access control system that is supposed to solve data sharing and data protection within a big data environment. Although the solution is a step forward in the technical data sharing aspect of CDS, it does not cover achieving CDS comprehensively.…”

“…The second solution identified is by Anisetti et al (2021), where they proposed an access control system that is supposed to solve data sharing and data protection within a big data environment. This solution provides a partial solution for CDS, as although it is mentioned that the system 'should' protect the privacy of sensitive data, it is not guaranteed.…”