Big Data and Security
Moderní technologie a bezpečnost (BSSn4411)
Modern technologies and conflict (CDSn4003)
Jan Kleiner
20.10.2021
Fakulta sociálních studií - Masarykova univerzita - Vysoké školy

Presentation outline
Big Data | A word cloud featuring "Big Data". This is licens… | Flickr
•Big Data – theoretical and methodological prism.
•Legitimate ways of use.
•Problematic ways of use.

Manyika et al. (2011, 1):
•„Big Data refers to datasets, whose size is beyond the ability of typical database software tools
to capture, store, manage, and analyse.“

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Manyika et al. (2011, 1):
•Definition intentionally subjective and moving.
•It also depends on a software tools and usual data size in a given sector.
•„… as technology advances over time, size of datasets that qualify as big data will also
increase.“
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Are data more valuable than oil?
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Three approaches (PWC, 2019)
•Market:
•Active markets for data are rare, mostly illegal.
•Shutterstock, Flicker.
•Cost:
•Straight-forward, how much does the data currently cost (e.g. CPC).
•Fails to capture future revenues a holder can get from the data.
•Income:
•Measure of cash flows the data are expected to generate.
•Around 2017 – Amazon, Google, Facebook – biggest net profits (mainly from advertising).
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Surveillance Capitalism (Zuboff, 2019)
Obsah obrázku text Popis byl vytvořen automaticky Age of Surveillance Capitalism


Behavioural Surpluss (Zuboff, 2019: 97)
The two instances of 'crisis exceptionalism' which wedged open the door for Google to
harvest our behaviour: 'The Age of Surveillance Capitalism', Chapters 3 and 4 – Zuboff,
2019 – Truth, Communication and Society

How to do research with Big Data?
•The distinction from „normal“ research is in the data collection.
•à How to collect „Big Data“?
•Google – Trends, Keyword Planner, 3rd parties – SEMRush, Keywordtool
•Social media – Twitter API, scrapers (Octoparse), Facepager
•Wikileaks
•Pastebin
•Cyber security – Shodan (academic licence, shodan trends)
•Open science repositories
•https://openscience.muni.cz/
•European legislation on open data and the re-use of public sector information
•https://ec.europa.eu/digital-single-market/en/european-legislation-reuse-public-sector-information
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•How is Big Data (e.g. searches from Google) different from „conventional“
survey/interview/experiment etc. data?
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There are pros as well as cons (Davidowitz, 2015)
•Overcome respondent bias (social desirability).
•Efficiency. Wider and deeper insight.
•Representativeness?
•Population of searchers? How big is it?
•Misformulated seed words.
•We need to intepret results with explicit limits and deliberation in the relation with
quantitative and qualitative methodologies.
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6 principles of scientific method
1.Empirically testable (through observations, data etc.)
2.Replicability.
3.Objectivity.
4.Transparency.
5.Falsifiability.
6.Logical consistency/coherency.
Abstract blurred public library with bookshelves

Challenges (Chen, 2018: 19-23)
•Complexity
•There is never too little data, only too little processing and analytical power.
•Social media – complex language issues (e.g., sentiment analysis), enormous scale of data.
•Data integrity? Not reliable due to lack of accessibility.
•Transparency? Black-box data algorithms.
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•Big Data search
•Keywords return too many results.
•The need of post-processing, indexing strategies.
•Lack of theoretical or scientific foundations for Big Data use in research à the need for huge
justification (see next slides).
•Other risks (caveats):
•Fake news, disinformation campaigns/psyops tarnishing.
•Technology as a catalyst for human behaviour.
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•„Big data do not constitute a panacea, and their dark side should never be ignored.“ (Chen, 2018:
22)


How to check for Big Data validity and reliability?
> Graph


•Measurement (construct) validity
•Covergent validity
•Measures of the same trait using different methods show agreement.
•Discriminant validity
•Different traits measured by the same method do not agree (any issues here?).
•Multi-trait Multi-method Matrix
•Test-retest reliability (repetition).
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Propaganda perception experiment
from Facebook discussion
Pizza perception experiment
Pizza Facebook
Propaganda perception experiment
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from Facebook discussion
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Pizza perception experiment
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Pizza Facebook
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Legitimate ways of use
•Army and law enforcement recruitment (see Jahedi, Wenger and Yeung, 2016).
•Studies on public perception (Kostakos, 2018).
•And others…
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Problematic ways of use
•Cambridge Analytica (see Isaak and Hanna, 2018) – Facebook data.
•Bulk surveillance (privacy vs. security debate) – e.g. PRISM programme exposed by Edward Snowden.
•Wikileaks.

References
•Manyika, J. et al. (2011). Executive summary: Big data: The next frontier for innovation,
competition, and productivity. McKinsey Global Institute. Available from:
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontie
r-for-Innovation.
•PWC. (2019). Putting a value on data. Available from:
https://www.pwc.co.uk/data-analytics/documents/putting-value-on-data.pdf.
•Jahedi, S., Wenger, J. W. a Yeung, D. (2016). Searching for Information online: Using Big Data to
Identify the Concerns of Potential Army Recruits. RAND Corp. ISBN: 978-0-8330-9414-8. Available
from: https://www.rand.org/pubs/research_reports/RR1197.html.
•Isaak, J. and Hanna, M. J. (2018). User Data Privacy: Facebook, Cambridge Analytica, and Privacy
Protection. Computer 51(8), pp. 56-59. DOI: 10.1109/MC.2018.3191268. Available from:
https://ieeexplore.ieee.org/abstract/document/8436400
•Davidowitz, S. S. a Varian, H. (2015). A Hands-on Guide to Google Data. pp. 9-25. (via Google
Scholar).
•Kostakos, P. (2018). Public Perceptions on Organised Crime, Mafia,and Terrorism: A Big Data
Analysis based onTwitter and Google Trends. International Journal of Cyber Criminology. 12(1). pp.
282-289. DOI: 10.5281/zenodo.1467919.
•Chen, S. (2018). Big Data in Computational Social Science and Humanities. Springer. DOI:
10.1007/978-3-319-95465-3.
•Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight For a Human Future at the New
Frontier of Power. London: Profile Books.
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Thank you for the attention. Questions and your presentations.