Advanced Cloud Privacy Threat Modeling

Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat modeling as a part of requirements engineering in secure software development provides a structured approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities in a system . This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in relation to processing sensitive data in cloud computing environments. It describes the modeling methodology that involved applying Method Engineering to specify characteristics of a cloud privacy threat modeling methodology, different steps in the proposed methodology and corresponding products. We believe that the extended methodology facilitates the application of a privacy-preserving cloud software development approach from requirements engineering to design

Privacy Threat Modeling for Emerging BiobankClouds

There is an increased amount of data produced by next generation sequencing (NGS) machines which demand scalable storageand analysis of genomic data. In order to cope with this huge amount of information, many biobanks are interested in cloudcomputing capabilities such as on-demand elasticity of computing power and storage capacity. There are several security andprivacy requirements mandated by personal data protection legislation which hinder biobanks from migrating big data generatedby the NGS machines. This paper describes the privacy requirements of platform-as-service BiobankClouds according to theEuropean Data Protection Directive (DPD). It identifies several key privacy threats which leave BiobankClouds vulnerable to anattack. This study benefits health-care application designers in the requirement elicitation cycle when building privacy-preservingBiobankCloud platforms.BioBankClou

Privacy Threat Modeling for Emerging BiobankClouds

There is an increased amount of data produced by next generation sequencing (NGS) machines which demand scalable storageand analysis of genomic data. In order to cope with this huge amount of information, many biobanks are interested in cloudcomputing capabilities such as on-demand elasticity of computing power and storage capacity. There are several security andprivacy requirements mandated by personal data protection legislation which hinder biobanks from migrating big data generatedby the NGS machines. This paper describes the privacy requirements of platform-as-service BiobankClouds according to theEuropean Data Protection Directive (DPD). It identifies several key privacy threats which leave BiobankClouds vulnerable to anattack. This study benefits health-care application designers in the requirement elicitation cycle when building privacy-preservingBiobankCloud platforms.BioBankClou