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Abstract for ISIPS 2008
The Requirements, Tasks and Solutions for a Privacy-Preserving
Counterintelligence System
By Jesus Mena, Chief Strategy Officer InferX Corporation
The Requirements: Collective Inferencing for Investigative Profiling
Suspicious scoring of avoidance behavior exists for counterintelligence and investigative data
mining. Pattern recognition and profiling is accomplished through suspicious scoring under
uncertainty via collective inferencing. In collective inferencing seemingly meaningless data
becomes meaningful by drawing inferences about everyone simultaneously without reliance on
guilt-by-association algorithms. In this type of analysis, shadowy and unusual interactions
and behavior are searched in order to generate risk scores from transactions, conditions,
events and sequences – as well as drawing out deviations from normal behavior.
A self-adaptive counterintelligence system for collective inferencing
The Tasks: Anonymous Text and Data Mining via Software Agents
Risk networks can be created for the identification of suspicious behavior patterns for the
creation of self-adaptive counterintelligence systems. Profiles are created from multiple data
sources in a totally anonymous manner without the need to centralize or move any data.
These techniques improve detection of threats and events where an embedded entity is not
available for social networking analysis for the discovery of ‘who knows whom where and
when’ via the use of guilt-by-association algorithms. Modeling algorithms can be used to
discover suspicious patterns, outlier or anomaly behavior as well as for the extraction of key
concepts from text.
The distillation of intelligence from structured databases and unstructured
documents with concept extractors
The Solutions: Advanced Analytics for Counterintelligence
Current advanced analytics and anonymous data networking technologies exists which allows
for the ability to analyze, structured databases and unstructured documents, email and
clickstream data for preemptive counterintelligence and reactive law enforcement
investigations. These investigative techniques use networks coupled with data and text
mining algorithms to discover suspicious behavior from multiple databases in a totally privacy
preserving manner.
The information analyzed may reside in different computer systems in various data formats
located anywhere in the world since the investigative analyzes and modeling can take place in
real-time over networks using anonymous pointers to the original location of the data. The
following graphic offers a view of a distributed analytic suite of tools which combine algorithms
for clustering, text and predictive analytics from multiple data sources from different locations
and formats:
A web-centric privacy-protecting software solution to counterintelligence
Presenter:
Jesus Mena is the curator of the “Web Mining” topic at Scholarpedia.org, he is the author of
five data mining books including “Homeland Security Techniques and Technologies” and
“Investigative Data Mining for Security and Criminal Detection” he has consulted with Sandia
Labs, the National Counter Terrorism Center and was the data mining contractor in the first
department-wide audit of all analytical systems at Department of Homeland Security for their
Office of Inspector General prior to joining InferX as its Chief Strategy Officer.