Secure data outsourcing in presence of the inference problem: issues and directions

Abstract

With the emergence of cloud computing paradigms, secure data outsourcing has become one of the crucial challenges which strongly imposes itself. Data owners place their data among cloud service providers in order to increase flexibility, optimize storage, enhance data manipulation and decrease processing time. Nevertheless, from a security point of view, access control is a major challenge in this situation seeing that the security policy of the data owner must be preserved when data is moved to the cloud. Nonetheless, the lack of a comprehensive and systematic review motivated us to construct this reviewing paper on this research problem. Here, we discuss current and emerging research on privacy and confidentiality concerns in data outsourcing and pinpoint potential issues that are still unresolved.

Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark

“…To address the overheads associated with MapReduce, several Spark-based approaches have been proposed in recent years [18,[25][26][27][28]. In [29], the authors proposed the INCOG-NITO framework for full-domain generalization using Spark RDDs.…”

Coupling of Inference and Access Controls to Ensure Privacy Protection

“…Another approach that seeks to ensure access control while preventing inference attacks by relying on semantics is proposed by Jebali et al [14]. This approach, intended for application in Cloud Computing, proposes dividing sensitive data into a set of partitions to be stored separately in the servers of cloud service providers.…”