As an example, suppose an image in
a tweet has generated a huge number
of followers. The knowns are the
image and the tweet avatar, and the
desired unknown is the location of
the image. Figure 4 shows the initial
input to the model and the resulting
paths that would help in obtaining this
information. The paths are arranged by
certainty. Figure 5 shows the expansion
of one of the paths with very high
we created 20 descriptive use cases.
Figure 2 shows the format we used for
A number of interviews were also
conducted in the U.K. to determine any
differences between the U. S. and U. K.
in the type of identification tasks. We
then created several generic use cases
based on frequent knowns and desired
unknowns that could be found using
attributes from both the real world and
the cyber world. Figure 3 contains a
description of one generic use case.
By considering these use cases in the
context of the SID model, some useful
identity features were discovered, such
as determining an individual’s ideology
as expressed in writing samples.
VISUALIZING THE SID MODEL
The visualization of the model is
currently under development at PNNL.
The visualization has a number of
uses. First, it helps the SID researchers
understand exactly what is already
in the model and where connections
between domains are scarce. Second,
the visualization is a great way to
demonstrate the SID work to the project
stakeholders. The next steps are to take
the visualization to the communities
that contributed to the generation of
the use cases and investigate how the
capabilities demonstrated in the SID
project can impact their work. Their
feedback will be used to adjust the
models and visualization and later to
design customized tools for various
Currently the interaction
visualization takes as input what
identity attribute(s) the end user
knows and the identity attribute(s)
desired. Given this information,
the user is shown a number of paths
where the dots represent an attribute
of a person and the lines between
the dots represent a transformation.
Transformations are either inferences
that can be drawn with some degree of
certainty from one attribute to another
or facts that lead to another attribute.
For example, knowing an individual’s
driver’s license number, a law
enforcement officer can use an official
resource to obtain the individual’s
address. An inference can be made
from the length of the hand to gender,
as men tend to have longer hands. The
domains (biometric, biographical,
cyber, and psychological) in the
visualization are shown as color-coded
dots, and the number of dots represent
the length of the path. The certainty of
the desired attributes is represented by
descriptors (very high, high, medium,
low, very low) at the top of the path.
Expanding a path gives the attributes
revealed at each step. The user can click
to obtain an explanation of how a given
attribute is obtained if desired.
Figure 4. Possible paths for finding location derived from Twitter images and Twitter avatar.
Figure 5. An expansion of the initial path that shows Twitter images can lead directly to location.
Figure 6. An explanation of how the latitude/longitude is obtained from images.