1. The PRISONER (Privacy-Respecting
Infras- tructure for Social Online Network
Experimental Research) architecture by
Hutton et al. (2012)
2. The Dataware system by Mortier et al.
(2013)
3. The Ma3tch (autonomous anonymous
analysis) technology.
PRISONER (Hutton et al, 2012) is an
architecture that was developed for conducting
social network experiments that preserve participant
privacy. The core element of the architecture is the
workflow manager unit that passes all data through a
data sanitiser before they are analysed or presented
to participants. The data sanitiser applies the
appropriate privacy-preserving transformations, that
are indicated by a privacy policy file.
Dataware (Mortier et al 2013) is a set of
technologies that were designed to enable people to
regain control over the digital data that is constantly
being created by and about them. Dataware provides
mechanisms for collating data that is held in
multiple locations (e.g. social media networks,
loyalty cards or banks) and making it available for
processing by third-parties, while retaining control
over the access to the data.
Ma3tch (Kroon, 2013) was originally built to
enable Financial Intelligence Units from various
countries to achieve virtual information integration
without infringing upon security, confidentiality,
privacy and/or data protection regulations. The
Ma3tch uses a ‘privacy by design’ framework that is
based on distributed agents to facilitate decentralized
but integrated information access, processing and
analysis. Relevant information and knowledge that is
distributed between autonomous organizations is
automatically detected and applied throughout the
network as soon as it emerges. Crucially the
sensitive raw data is never shared, only anonymized
standardized information is shared.
Each of these tools presents a different approach
to the problem of privacy preserving data handling,
and while much work is yet to be done, they do at
least provide examples to show that IMR can be
done without risking violations of privacy and
human dignity. As such, development of these tools,
and others like them, provides a clear signal that
there is no excuse for breaking the codes of ethical
research conduct. They also provide beacons of
research integrity to raise confidence and trust from
the public.
6 CONCLUSIONS
Based on the high level of popular and media
attention currently directed at anything related to
social media or the internet, IMR is currently
receiving a greater level of media scrutiny than most
other types of research. In combination with the
uncertainties that still exist around various aspects of
IMR ethics, this media attention carries the risk for
IMR of triggering a controversy and public backlash
similar to the one that hit GM crops in Europe in the
1990s. In order to avoid such a controversy it is
essential to retain the confidence and trust of the
public which, in the light of the “Snowden
revelations”, depends heavily on the use of
responsible safeguards for privacy and ethical
treatment of human data.
ACKNOWLEDGEMENTS
This work forms part of the CaSMa project
supported by ESRC grant ES/M00161X/1. For more
information about the CaSMa project, see
http://casma.wp.horizon.ac.uk/ .
REFERENCES
Andrews, L., 2013. I Know Who You Are and I Saw What
You Did: Social Networks and the Death of Privacy.
Free Press, New York, NY, USA.
Barbaro, M. and Zeller, T., 2006. A face is exposed for
AOL searcher no. 4417749, New York Times, August
9, 2006.
BBC News, 2014. OKCupid experiments with ‘bad’dating
matches. BBC News, 29 July 2014.
Biotechnology and the European Public Concerted Action
Group, 1998. Europe ambivalent on biotechnology.
Nature 387, 845–847.
Booth, R., 2014. Facebook reveals news feed experiment
to control emotions. The Guardian, Mon 30 June,
2014.
British Psychological Society, 2013. Ethics Guidelines for
Internet-mediated Research. INF206/1.2013.
Carr, S., Levidow, L., 2000. Exploring the Links Between
Science, Risk, Uncertainty, and Ethics in Regulatory
Controversies About Genetically Modified Crops.
Journal of Agricultural and Environmental Ethics, 12,
1, 29-39.
Cooper, R.G., Kleinschmidt, E.J., 1987. Success factors in
new pro- duct innovation. Industrial Marketing
Management 16, 215–233.
Dragland, Å., 2013; http://www.sintef.no/home/Press-
Room/Research-News/Big-Data--for-better-or-worse/
Dwyer, L., Mellor, R., 1991. New product process
ResearchEthicsandPublicTrust,PreconditionsforContinuedGrowthofInternetMediatedResearch-PublicConfidence
inInternetMediateResearch
167