Access Control for Data Integration in Presence of Data Dependencies

International audienceDefining access control policies in a data integration scenario is a challenging task. In such a scenario typically each source specifies its local access control policy and cannot anticipate data inferences that can arise when data is integrated at the mediator level. Inferences, e.g., using functional dependencies, can allow malicious users to obtain, at the mediator level, prohibited information by linking multiple queries and thus violating the local policies. In this paper, we propose a framework, i.e., a methodology and a set of algorithms, to prevent such violations. First, we use a graph-based approach to identify sets of queries, called violating transactions, and then we propose an approach to forbid the execution of those transactions by identifying additional access control rules that should be added to the mediator. We also state the complexity of the algorithms and discuss a set of experiments we conducted by using both real and synthetic datasets. Tests also confirm the complexity and upper bounds in worst-case scenarios of the proposed algorithms

Data Security and Privacy in the Cloud

Relying on the cloud for storing data and performing computations has become a popular solution in today\u2019s society, which demands large data collections and/or analysis over them to be readily available, for example, to make knowledge-based decisions. While bringing undeniable benefits to both data owners and end users accessing the outsourced data, moving to the cloud raises a number of issues, ranging from choosing the most suitable cloud provider for outsourcing to effectively protecting data and computation results. In this paper, we discuss the main issues related to data protection arising when data and/or computations over them are moved to the cloud. We also illustrate possible solutions and approaches for addressing such issues