393
However, consider the counterargument—especially from a government’s point
of view—that it is difficult to know exactly how data could be legitimately used.
Many types of data are only useful when combined with other data that may not
yet exist. The value of these data grows over time. For example, by “joining”
independently collected energy use, water use, demographic, building-type, and
process level data, it might be possible for us to precisely understand how to help
make a given business, household, or process more efficient and sustainable.
What if this exact type of combined use of the data was not anticipated at the time
of its collection several decades earlier, and the person or business about whom
the data was originally collected no longer exists to seek consent? Is it ethical to
make this use of the data, or not?
Need-to-Know is a precautionary data access principle that minimizes access to
sensitive data to only those persons with a legitimate purpose. This access minimization, along with the screening and identification of persons accessing data, provides a basic level of data protection along with deterrence of and accountability for
unethical or illegal employment of the data. Security through Obscurity is a tactic,
often unintentional or implicit, of protecting data by minimizing its findability,
accessibility, and interoperability. Many government agencies practice security
through obscurity, as do many private individuals. Government agencies do this
primarily as a cost savings measure and precautionary measure because they do not
have a funded mandate to provide FAIR data management services that also protect
data properly.
Need-to-know is integral to many data protection processes, most notably classified and categorized data access. Classified data is only available to highly qualified
and exhaustively screened individuals like military personnel or high-ranking officials. In the USA, Protected Critical Infrastructure Information (PCII) is a
government- enforced national security categorization (not classification) originating after the September 11th, 2001 attacks. Under this categorization, some food
energy or water infrastructure data is available only on a need-to-know basis.
Research may not qualify as a need-to-know, depending on its utility and on who is
doing the research. PCII data specifically includes the precise “target list” locations
of key pumps, transformers, storage depots, pipelines, or transmission lines for
food, energy, and water.
The bedrock and central Data Ethics principle is that the benefits of collection
and use of private or sensitive data must outweigh the risks from the perspective of
the object of the data collection (that is, the people and ecosystems involved). This
principle will be familiar to any university researcher who has undergone the
Institutional Review Board (IRB) process to scrutinize their data collection methods.
In other words, the privacy of data must be balanced against the legitimate benefit
of the use of the data—that is, the utility of the data. One cannot usually optimize
both utility and privacy, because they are in tension in most cases. For instance,
water efficiency researchers could derive significant utility by accessing individual
customers’ water and energy use data, but this release could be sensitive and risky
for some individual customers and so most US States grant a right of privacy to the
14 Data
However, consider the counterargument—especially from a government’s point
of view—that it is difficult to know exactly how data could be legitimately used.
Many types of data are only useful when combined with other data that may not
yet exist. The value of these data grows over time. For example, by “joining”
independently collected energy use, water use, demographic, building-type, and
process level data, it might be possible for us to precisely understand how to help
make a given business, household, or process more efficient and sustainable.
What if this exact type of combined use of the data was not anticipated at the time
of its collection several decades earlier, and the person or business about whom
the data was originally collected no longer exists to seek consent? Is it ethical to
make this use of the data, or not?
Need-to-Know is a precautionary data access principle that minimizes access to
sensitive data to only those persons with a legitimate purpose. This access minimization, along with the screening and identification of persons accessing data, provides a basic level of data protection along with deterrence of and accountability for
unethical or illegal employment of the data. Security through Obscurity is a tactic,
often unintentional or implicit, of protecting data by minimizing its findability,
accessibility, and interoperability. Many government agencies practice security
through obscurity, as do many private individuals. Government agencies do this
primarily as a cost savings measure and precautionary measure because they do not
have a funded mandate to provide FAIR data management services that also protect
data properly.
Need-to-know is integral to many data protection processes, most notably classified and categorized data access. Classified data is only available to highly qualified
and exhaustively screened individuals like military personnel or high-ranking officials. In the USA, Protected Critical Infrastructure Information (PCII) is a
government- enforced national security categorization (not classification) originating after the September 11th, 2001 attacks. Under this categorization, some food
energy or water infrastructure data is available only on a need-to-know basis.
Research may not qualify as a need-to-know, depending on its utility and on who is
doing the research. PCII data specifically includes the precise “target list” locations
of key pumps, transformers, storage depots, pipelines, or transmission lines for
food, energy, and water.
The bedrock and central Data Ethics principle is that the benefits of collection
and use of private or sensitive data must outweigh the risks from the perspective of
the object of the data collection (that is, the people and ecosystems involved). This
principle will be familiar to any university researcher who has undergone the
Institutional Review Board (IRB) process to scrutinize their data collection methods.
In other words, the privacy of data must be balanced against the legitimate benefit
of the use of the data—that is, the utility of the data. One cannot usually optimize
both utility and privacy, because they are in tension in most cases. For instance,
water efficiency researchers could derive significant utility by accessing individual
customers’ water and energy use data, but this release could be sensitive and risky
for some individual customers and so most US States grant a right of privacy to the
14 Data