Privacy Threat Modeling for Emerging BiobankClouds

Gholami, Ali; Lind, Anna-Sara; Reichel, Jane; Litton, Jan-Eric; Edlund, Åke; Laure, Erwin

doi:10.1016/j.procs.2014.08.073

Cited by 14 publications

(13 citation statements)

References 9 publications

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“…The system allows defining different roles with different access privileges. In designing the system, we applied the Cloud Privacy Threat Modeling [15] approach to identify the privacy requirements of processing sensitive biomedical data. This model implements a data management policy that aligns with the European data protection directive.…”

Section: Security Modelmentioning

confidence: 99%

BiobankCloud: A Platform for the Secure Storage, Sharing, and Processing of Large Biomedical Data Sets

Bessani

Brandt

Bux

et al. 2016

Lecture Notes in Computer Science

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Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.

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Section: Security Modelmentioning

confidence: 99%

BiobankCloud: A Platform for the Secure Storage, Sharing, and Processing of Large Biomedical Data Sets

Bessani

Brandt

Bux

et al. 2016

Lecture Notes in Computer Science

Self Cite

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“…In [55], the design and implementation of a security framework for BiobankCloud, a platform that supports the secure storage and processing of genomic data in cloud computing environments, has been discussed. The proposed framework is built on the cloud privacy threat modeling approach [54,56] which is used to define the privacy threat model for processing next-generation sequencing data according to the DPD [2]. This solution includes a flexible two-factor authentication and an RBAC access control mechanism, in addition to auditing mechanisms to ensure that the requirements of the DPD are fulfilled.…”

Section: Privacy-preserving Big Data Solutions In the Cloudmentioning

confidence: 99%

Big Data Security and Privacy Issues in the CLOUD

Gholami¹,

Laure²

2016

IJNSA

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Many organizations demand efficient solutions to store and analyze huge amount of information. Cloud computing as an enabler provides scalable resources and significant economic benefits in the form of reduced operational costs. This paradigm raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-ofthe-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference architecture orchestration, resource control, physical resource, and cloud service management layers, in addition to reviewing the recent developments for enhancing the Apache Hadoop security as one of the most deployed big data infrastructures. We also outline the frontier research on privacy-preserving data-intensive applications in cloud computing such as privacy threat modeling and privacy enhancing solutions.

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“…In the privacy regulatory compliance step, learning sessions with privacy experts, end-users and requirements engineers facilitates the elicitation of privacy requirements (PR). For example, in the EU DPD some of the privacy requirements are: lawfulness, informed consent, purpose binding, transparency, data minimization, data accuracy, data security, and accountability [6]. Each of the requirements that are identified will be labeled with an identifier, e.g., (PRi), name and description to be used in later stages.…”

Section: Privacy Regulatory Compliancementioning

confidence: 99%