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samenvatting - big data security
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Summary of the piece 'The (Big) Data security assembly: Knowledge and critique' by Aradau & Blanke (2015). About views on Big Data and its criticisms.
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16 - The (Big) Data-security assemblage: Knowledge and critique - Aradau &Blanke (2015)
Binnen het debat over beveiligingspraktijken voor Big Data moet er volgens de auteurs meer
aandacht uitgaan naar kritische kennis uit de computer- en informatiewetenschappen. Deze
kennis kan gebruikt worden om veelvoorkomende beweringen van beveiligingsprofessionals
over Big Data aan te vechten.
• Hoe worden de besproken opvattingen over Big Data ontkracht met informatie uit de
computer- en informatiewetenschappen?
- intelligence agencies: big data organizations that employ data-driven methods to anticipate
future dangers
→ a collaboration between social and computer scientists can help go beyond the inscrutability
of algorithmic methods in security practices
- three moves that recast existing critical engagements with data-driven security:
1. from data/metadata distinctions to production of data as a complex epistemic entity
2. from a computational turn in surveillance to the division of labour between humans and
computers in socio-technical assemblages
3. from an underlying logic of algorithms to algorithmic practices and methods in security
analytics
(Meta)data and the remaking of security knowledge
- metadata: a set of data that describes and gives information about other data (content)
→ telephony metadata oozes with meaning, which makes distinction from content problematic
- in knowledge engineering, content is anything that can be expressed digitally
- data-information-knowledge (DIK): hierarchy starts with raw data and systematically builds
information and finally knowledge
- data and metadata both refer to practices of knowledge production, which simultaneously
draw boundaries between structured/unstructured data, information and knowledge
- theories of (meta)data production and the critique of the DIK hierarchy are important moves
that challenge the justifactory discourses of security professionals
Big data as artificial intelligence
- second argument in the controversies about digital surveillance had been formulated in terms
of controlled entries to and views onto data
- mass surveillance: collecting/analysing data about many people instead of individuals
- assumption: there is no surveillance when data is not seen by a human (but just a computer)
→ humans only come at the end of the data processing, so only see very little: better privacy
- new division of labour: humans and machines are brought together in the same infrastructures
to process the data
- topic modelling: unsupervised learning technique which auto-summarizes a collection of
documents into a number of common topics (human-computer assemblage)
- security analytics enrol computers, AI practices and data scientists
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