Christine Task, "Data, Privacy---and the Interactions Between Them"

Data deidentification aims to provide data owners with
edible cake: to allow them to freely use, share, store and publicly
release sensitive record data without risking the privacy of any of
the individuals in the data set.   And, surprisingly, given some
constraints, that’s not impossible to do.    However, the behavior
of a deidentification algorithm depends on the distribution of the
data itself.   



Privacy research often treats data as a black box---omitting formal
data-dependent utility analysis, evaluating over simple homogeneous
test data, and using simple aggregate performance metrics.   As a
result, there’s less work formally exploring detailed algorithm
interactions with realistic data contexts.   This can result in
tangible equity and bias harms when these technologies are
deployed; this is true even of deidentification techniques such as
cell-suppression which have been in widespread use for decades.  
At worst, diverse subpopulations can be unintentionally erased from
the deidentified data. 



Successful engineering requires understanding both the properties
of the machine and how it responds to its running environment.  In
this talk I’ll provide a basic outline of distribution properties
such as feature correlations, diverse subpopulations, deterministic
edit constraints, and feature space qualities (cardinality,
ordinality), that may impact algorithm behavior in real world
contexts.  I’ll then use new (publicly
available) tools from the National Institute of Standards
and Technology to show unprecedentedly detailed performance
analysis for a spectrum of recent and historic deidentification
techniques on diverse
community benchmark data.   We’ll combine the two and
consider a few basic rules that help explain the behavior of
different techniques in terms of data distribution properties.  But
we’re very far from explaining everything—I’ll describe some
potential next steps on the path to well-engineered data privacy
technology that I hope future research will explore.  A path I hope
some CERIAS members might join
us on later this year. 

This talk will be accessible to anyone who’s interested—no
background in statistics, data, or recognition of any of the above
jargon is required.