Big data workflow management systems (BDWMS)s have recently emerged as popular data analytics platforms to conduct large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems, such as VC3 and Opaque, were developed to address security problems, they are limited to specific domains such as Map-Reduce-style and SQL query workflows. A generic secure framework for BDWMSs is still missing. In this article, we propose SecDATAVIEW, a distributed BDWMS that employs heterogeneous workers, such as Intel SGX and AMD SEV, to protect both workflow and workflow data execution, addressing three major security challenges: (1) Reducing the TCB size of the big data workflow management system in the untrusted cloud by leveraging the hardware-assisted TEE and software attestation; (2) Supporting Java-written workflow tasks to overcome the limitation of SGX’s lack of support for Java programs; and (3) Reducing the adverse impact of SGX enclave memory paging overhead through a “Hybrid” workflow task scheduling system that selectively deploys sensitive tasks to a mix of SGX and SEV worker nodes. Our experimental results show that SecDATAVIEW imposes moderate overhead on the workflow execution time.

The notion of collaborative scientific workflow is coined to address the increasing need for collaborative data analytics. In collaborative environments, access control policies are necessary for controlling the sharing of workflows, data products, and provenance information among collaborating parties. In particular, the protection of workflow provenance is critical because it often encodes the detailed protocol of a scientific experiment and carries the intellectual property of the respective stakeholders. In addition, since scientific workflows often evolve quickly, the corresponding access control policies for workflow provenance have to evolve as well. It is important to ensure that the evolution of workflow provenance access control policies maintain certain properties, in order to guarantee the correctness and performance of the corresponding policy enforcement. In this paper, we 1) propose a role-based access control model for scientific workflow provenance; 2) define three quality requirements for scientific workflow provenance access control policies - consistency, completeness, and conciseness; 3) develop a mechanism mapping from specifications of workflows to their counterparts in a provenance that preserves such quality properties, and 4) conduct a case study on a scientific workflow for autism behavioral data analysis that demonstrates the feasibility of our proposed analysis algorithms.

Big data workflow management systems (BDWFMSs) have recently emerged as popular platforms to perform large-scale data analytics in the cloud. However, the protection of data confidentiality and secure execution of workflow applications remains an important and challenging problem. Although a few data analytics systems were developed to address this problem, they are limited to specific structures such as Map-Reduce-style workflows and SQL queries. This paper proposes SecDATAVIEW, a BDWFMS that leverages Intel Software Guard eXtensions (SGX) and AMD Secure Encrypted Virtualization (SEV) to develop a heterogeneous trusted execution environment for workflows. SecDATAVIEW aims to (1) provide the confidentiality and integrity of code and data for workflows running on public untrusted clouds, (2) minimize the TCB size for a BDWFMS, (3) enable the trade-off between security and performance for workflows, and (4) support the execution of Java-based workflow tasks in SGX. Our experimental results show that SecDATAVIEW imposes 1.69x to 2.62x overhead on workflow execution time on SGX worker nodes, 1.04x to 1.29x overhead on SEV worker nodes, and 1.20x to 1.43x overhead on a heterogeneous setting in which both SGX and SEV worker nodes are used.

Early intervention in autism, although deemed as essential, has high variance in the outcome attained, partially due to complex interaction between multitude of factors and variables involved, and the lack of systematic study to untangle their influences in the outcome. Therefore, pairing set of interventions with an individual children to cater for their need remains highly challenging. From the perspective of parents, unknown factors emanate from their unfamiliarity with what interventions are out there and why. From the perspective of caregivers, it is critical to understand unique attributes of the individual children develop over time. There is a scarcity of exploration of interactions between attributes specific to a child, family characteristics and therapeutic, medical and educational services. In this research, we aim to bridge the gap. In this study, we identify predictive features pertaining to each individual child and how they interact responding to different interventions and services. We have studied temporal data and model improvement/regression outcomes at different timestamped milestones and overlayed a model to aid parents and caregivers in coming up with pragmatic intervention plan. We propose a scientific workflow to automate the modeling process and rely on DATAVIEW to guarantee computational reproducibility and data fidelity. We use data collected by SFARI dataset for evaluation. To the best of our knowledge, this is first-time amalgamation between the Autism Health informatics community and the Workflow community; and this is the first-time study that combines prediction methods applied on Autism Spectrum Disorder (ASD) Phenotype data to provide guidance to parents and caregivers.

Stream control transmission protocol (SCTP) is newly emerged protocol, which combined good qualities of TCP and UDP. It is a reliable message oriented protocol providing multistreaming and multihoming as well. The congestion control scheme of SCTP is more or less similar to that of TCP with the exception of the fast recovery algorithm. The performance of SCTP over the Internet and satellite links is improved as compared with TCP. However, the congestion control scheme of SCTP over high latency broadband networks needs further refinement in case of multiple packet losses on a link. We propose a new congestion control for SCTP and proved that performance of SCTP has significantly improved.