48 research outputs found
A primer on provenance
Better understanding data requires tracking its history and context.</jats:p
Viewpoint | Personal Data and the Internet of Things: It is time to care about digital provenance
The Internet of Things promises a connected environment reacting to and
addressing our every need, but based on the assumption that all of our
movements and words can be recorded and analysed to achieve this end.
Ubiquitous surveillance is also a precondition for most dystopian societies,
both real and fictional. How our personal data is processed and consumed in an
ever more connected world must imperatively be made transparent, and more
effective technical solutions than those currently on offer, to manage personal
data must urgently be investigated.Comment: 3 pages, 0 figures, preprint for Communication of the AC
Sharing and Preserving Computational Analyses for Posterity with encapsulator
Open data and open-source software may be part of the solution to science's
"reproducibility crisis", but they are insufficient to guarantee
reproducibility. Requiring minimal end-user expertise, encapsulator creates a
"time capsule" with reproducible code in a self-contained computational
environment. encapsulator provides end-users with a fully-featured desktop
environment for reproducible research.Comment: 11 pages, 6 figure
Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier
As universities recognize the inherent value in the data they collect and
hold, they encounter unforeseen challenges in stewarding those data in ways
that balance accountability, transparency, and protection of privacy, academic
freedom, and intellectual property. Two parallel developments in academic data
collection are converging: (1) open access requirements, whereby researchers
must provide access to their data as a condition of obtaining grant funding or
publishing results in journals; and (2) the vast accumulation of 'grey data'
about individuals in their daily activities of research, teaching, learning,
services, and administration. The boundaries between research and grey data are
blurring, making it more difficult to assess the risks and responsibilities
associated with any data collection. Many sets of data, both research and grey,
fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities
are exploiting these data for research, learning analytics, faculty evaluation,
strategic decisions, and other sensitive matters. Commercial entities are
besieging universities with requests for access to data or for partnerships to
mine them. The privacy frontier facing research universities spans open access
practices, uses and misuses of data, public records requests, cyber risk, and
curating data for privacy protection. This paper explores the competing values
inherent in data stewardship and makes recommendations for practice, drawing on
the pioneering work of the University of California in privacy and information
security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201
Information Flow Audit for Transparency and Compliance in the Handling of Personal Data
This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/IC2EW.2016.29The adoption of cloud computing is increasing and its use is becoming widespread in many sectors. As the proportion of services provided using cloud computing increases, legal and regulatory issues are becoming more significant. In this paper we explore how an Information Flow Audit (IFA) mechanism, that provides key data regarding provenance, can be used to verify compliance with regulatory and contractual duty, and survey potential extensions. We explore the use of IFA for such a purpose through a smart electricity metering use case derived from a French Data Protection Agency recommendation.This work was supported by UK Engineering and Physical Sciences Research Council grant EP/K011510 CloudSafetyNet: End-to-End Application Security in the Cloud. We acknowledge the support of Microsoft through the Microsoft Cloud Computing Research Centre
The Deployment of an Enhanced Model-Driven Architecture for Business Process Management
Business systems these days need to be agile to address the needs of a
changing world. Business modelling requires business process management to be
highly adaptable with the ability to support dynamic workflows,
inter-application integration (potentially between businesses) and process
reconfiguration. Designing systems with the in-built ability to cater for
evolution is also becoming critical to their success. To handle change, systems
need the capability to adapt as and when necessary to changes in users
requirements. Allowing systems to be self-describing is one way to facilitate
this. Using our implementation of a self-describing system, a so-called
description-driven approach, new versions of data structures or processes can
be created alongside older versions providing a log of changes to the
underlying data schema and enabling the gathering of traceable (provenance)
data. The CRISTAL software, which originated at CERN for handling physics data,
uses versions of stored descriptions to define versions of data and workflows
which can be evolved over time and thereby to handle evolving system needs. It
has been customised for use in business applications as the Agilium-NG product.
This paper reports on how the Agilium-NG software has enabled the deployment of
an unique business process management solution that can be dynamically evolved
to cater for changing user requirement.Comment: 11 pages, 4 figures, 1 table, 22nd International Database Engineering
& Applications Symposium (IDEAS 2018). arXiv admin note: text overlap with
arXiv:1402.5764, arXiv:1402.5753, arXiv:1502.0154