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Register a new userPPaaS: Privacy Preservation as a Service
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Added by
Mahawaga Arachchige Pathum Chamikara
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is one of the important components in data science. However, due to privacy concerns, data should be enforced with strong privacy guarantees before sharing. Different privacy-preserving approaches were developed for privacy preserving data sharing; however, identifying the best privacy-preservation approach for the privacy-preservation of a certain dataset is still a challenge. Different parameters can influence the efficacy of the process, such as the characteristics of the input dataset, the strength of the privacy-preservation approach, and the expected level of utility of the resulting dataset (on the corresponding data mining application such as classification). This paper presents a framework named underline{P}rivacy underline{P}reservation underline{a}s underline{a} underline{S}ervice (PPaaS) to reduce this complexity. The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset. PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.
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Aiming at the privacy preservation of dynamic Web service composition, this paper proposes a SDN-based runtime security enforcement approach for privacy preservation of dynamic Web service composition. The main idea of this approach is that the owner of service composition leverages the security policy model (SPM) to define the access control relationships that service composition must comply with in the application plane, then SPM model is transformed into the low-level security policy model (RSPM) containing the information of SDN data plane, and RSPM model is uploaded into the SDN controller. After uploading, the virtual machine access control algorithm integrated in the SDN controller monitors all of access requests towards service composition at runtime. Only the access requests that meet the definition of RSPM model can be forwarded to the target terminal. Any access requests that do not meet the definition of RSPM model will be automatically blocked by Openflow switches or deleted by SDN controller, Thus, this approach can effectively solve the problems of network-layer illegal accesses, identity theft attacks and service leakages when Web service composition is running. In order to verify the feasibility of this approach, this paper implements an experimental system by using POX controller and Mininet virtual network simulator, and evaluates the effectiveness and performance of this approach by using this system. The final experimental results show that the method is completely effective, and the method can always get the correct calculation results in an acceptable time when the scale of RSPM model is gradually increasing.
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