Toward Reliable and Secure Data Access for Big Data Service

Big Data ◽  
2016 ◽  
pp. 243-256
Keyword(s):  
Big Data ◽  
Data Access ◽  
Data Service ◽  
Secure Data

scholarly journals US Data Access and the Commission for Evidence-based Policymaking

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amy O'Hara
Keyword(s):  
Differential Privacy ◽  
Population Level ◽  
Data Access ◽  
Evidence Based ◽  
Data Service ◽  
Policy Makers ◽  
Combining Data ◽  
Secure Data ◽  
Building Activities ◽  
The Uk

IntroductionIn September 2017, the bipartisan Commission for Evidence-based Policymaking released twenty-two recommendations to improve secure data access for evidence building activities involving population-level government files. Many of the files are siloed in government agencies. The commission deliberated over eighteen months to understand the risks and barriers to broader data use. Objectives and ApproachI will describe the Commission’s charge and review its recommendations, in context of US laws and privacy debates. I will compare the report’s recommendations and implications to laws and initiatives in other countries. The report calls for the establishment of a National Secure Data Service (NSDS), which has the potential to transform the data sharing environment for federal agencies, policy makers, and researchers. The report suggests more extensive use of differential privacy and secure multiparty computation to protect privacy. I will describe how the current environment could change depending on how the recommendations are implemented. ResultsThe Commission was established under one administration, but the recommendations were released under another. Despite the political and budget uncertainty in Washington, a bill was introduced and passed in the House in November 2017 to implement some recommendations. I will summarize the actions to be taken if the bill becomes law, including directives on learning agendas to prioritize and coordinate evidence-building activities across government, the roles of chief evaluation and chief data officers, and formation of an advisory committee to plan a NSDS. I will describe benefits that could follow from directives in the bill, including transparency about uses of administrative data, development of guidance to assess the risk when combining data sources, and minimization of the risk of publicly releasing de-identified data. Conclusion/ImplicationsThe US may develop a national secure data service to support evaluations and policymaking. The recommendations are akin to the UK Data Service. Some recommendations are straightforward, others need years of planning and technical breakthroughs, and all require political buy-in and funding.

scholarly journals Computational storage: an efficient and scalable platform for big data and HPC applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mahdi Torabzadehkashi ◽  
Siavash Rezaei ◽  
Ali HeydariGorji ◽  
Hosein Bobarshad ◽  
Vladimir Alves ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance ◽  
Distributed Processing ◽  
Data Access ◽  
Distributed Applications ◽  
Process Data ◽  
Storage Devices ◽  
Hadoop Mapreduce ◽  
Big Data Applications ◽  
Application Processor

AbstractIn the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.

Secure Data Access and Sharing Scheme for Cloud Storage

2016 ◽  
Vol 96 (4) ◽  
pp. 5295-5314 ◽  
Author(s):  
Xiong Li ◽  
Saru Kumari ◽  
Jian Shen ◽  
Fan Wu ◽  
Caisen Chen ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Data Access ◽  
Sharing Scheme ◽  
Secure Data

scholarly journals IoT Based Decision Making System to Improve Veracity of Big Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 63 ◽  
Author(s):  
R Revathy ◽  
R Aroul Canessane
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Streams ◽  
Data Cleaning ◽  
Data Handling ◽  
Secure Data ◽  
Decision Making System ◽  
Data Investigation ◽  
Impact Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.  

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