Technology has made it possible for us to collect data in ways and quantities that many of us had
never anticipated. We can now analyze data in novel ways to detect trends and findings that have
influenced important business decisions thanks to more powerful data analytics tools. These techniques
enable us to discover previously unnoticed correlations between data.
There are multiple types of open data with potential applications, such as Statistics, Weather
and Science. Statistics data important socioeconomic indicators, like the census, are created by statistics
offices. Weather data are kinds of data used to comprehend and make climate and weather predictions.
While, Science data is created during scientific investigation, from astronomy to biology.
The privacy of individuals can be infringed by open data projects in a variety of ways. Personal
information about an individual should not be included in open data, and this is usually the case. Yet, if
enough details about a person are made public, it's easy to determine who they are. Cross-referencing
and connecting data from various databases and datasets can be used to achieve this. The ability to
identify and link information to particular people has come under fresh threat because of declining data
gathering costs and sophisticated analysis techniques like big data.
Because people do not anticipate that others will record and analyze their behavior in a way that
is consistent with modern analytics techniques, Helen Nissenbaum (1998) argues that people do indeed
maintain a right to privacy in public spaces ("Privacy in an Information Age," p. 595). Hence, by creating a
detailed portrait of a person without that person's knowledge or agreement, the collecting and
aggregation of "public" data from open datasets and other databases might infringe that person's right
to privacy.
Week 2 Briefing Statement: Privacy Implications
of OPEN Data
While publishing open datasets, technical tools are also employed to safeguard the privacy of
the public. These technologies are essential for preserving privacy in publicly available datasets.
Data aggregation is the technique of grouping and publishing precise data as statistics or
information. Due to the inability to identify specific persons when data is rolled up into pools with
predetermined amount thresholds, this approach improves individual privacy.
The use of policy is another way to safeguard privacy. Access and re-use limits are the two main
policy measures that can be used to preserve informational privacy. Although these limitations can only
be used in certain circumstances, they assist place open data inside the parameters of the current policy
instruments.
Restricting how data is used is the other policy option that may be used to preserve data privacy.
Licensing agreements that are linked to the data can be used to apply these limitations. Data owners
have the option to control how their data is distributed by those who have access to it thanks to licenses.
The relevant government body in the case of open data is the rightful owner of the information and may
use licenses or crown copyright to safeguard its property in Canada.
References
Canadian Internet Policy and Public Interest Clinic. (2016). “Open Data, Open
Citizens?” https://cippic.ca/en/open_governance/open_data_and_privacy
Opendatasoft (2017). “What is Open Data?” https://www.opendatasoft.com/en/blog/what-is-
open-data
Information and Privacy Commissioner of Ontario. (2017) “Open Government and Protecting
Privacy” https://www.ipc.on.ca/wp-content/uploads/2017/03/open-gov-privacy-1.pdf
