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evaluating data-driven applications and solutions - Coggle Diagram
evaluating data-driven applications and solutions
the explosion in data
large amount of new data
large usage of data
new data centres
new usage of data
large companies centred around data storage
evolving data security impacts
higher levels of encryption to hide personal data
organisations can access personal location information
data can be collected by third party companies
information as a commodity
information can be traded between companies
information can be mined by companies through process's like loyalty cards
information can be duplicated between services to streamline the usage for the user.
information social evolution,
Australia card 1980s
solve tax avoidance
solve illegal immigration
a super key to the holder, allowing access to government systems
rigorous testing required due to fraudulent registers
everyone in Australia was required to own one
the card was required to be presented in a lot of services
myGov website 2010s
used to streamline online services
requires specific personal data to verify its user
potential data surveillance
challenge based approach
retention and disposal of data
sensitive or personal information is not required
companies can be required by law to retain certain types of data
laws can allow certain organisations to access data like criminal law enforcement
in the future, laws will expands how much data is retained.
de-identification of organisational data
rapid growth of data
freely give access to there data for the "good of all"
data is deemed non sensitive in nature
freely usable for all without fear of copyright or licensing restrictions
de-identify the dateset for open consumption
quality assure de-identification measures through consultation with privacy experts
review how the Australian privacy principles have been taken into account
document a data dictionary to help end users to use the dataset
when datasets are non identifiable, they get released by the government to the public