may 29, 2002
DESCRIPTION
Emerging Technologies, Homeland Security and the Privacy/Security Trade-off Dr. Phil Hayes & Dr. Ganesh Mani. May 29, 2002. Agenda. Background Current Technologies and their Limitations New / Emerging Technologies (esp. Intelligent Matching) Summary and Conclusions. Background. - PowerPoint PPT PresentationTRANSCRIPT
Proprietary and Confidential
Emerging Technologies, Homeland Security and the Privacy/Security Trade-off
Dr. Phil Hayes & Dr. Ganesh ManiMay 29, 2002
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Agenda
• Background
• Current Technologies and their Limitations
• New / Emerging Technologies (esp. Intelligent Matching)
• Summary and Conclusions
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Background
• Privacy vs. Security (two sides of the same coin?)• Spotlight on homeland security, expanded
wiretapping provisions, USAPATRIOT Act, etc.• The role of the Internet is broadly changing the
semantics of privacy– e.g., Allegheny county property records– Driving by somebody’s home vs. putting a webcam outside
• Key is finding the right trade-off• The Challenge: for local, state, and federal
governments to provide maximum Public Safety in the most benign and cost effective manner
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A Few Tenets
• Increasing security implies increased information.• Increased information does not need to imply
decreased privacy• Privacy is a direct function of the use of information• Automated solutions operating on better information
should result in increased privacy and increased security
• Automation can support privacy/convenience tradeoffs
• Ben Franklin: “People who give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety.”
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Financial Security
• Ensuring integrity of capital markets– Monitoring suspicious security transactions (equities,
options, etc.)– Number of trades is high, post-decimalization
• Anti-money Laundering– USA PATRIOT Act– Cross-border transactions– Linking financial transactions with other transactions
(purchase of hazardous chemicals, e.g.)
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Current / Existing Technologies
• Instantaneous transmission of information via the Internet and private networks
• Database with special-purpose scripts
• Data mining (techniques that work well with noisy, incomplete data are rare)
• Event-based triggers
• Automated face recognition, voice recognition and other biometric techniques
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Shortcomings of Current Techniques
• Excessive false positives• Expensive manual processes• Exposed and unprotected personal information• Not scalable• Inability to use prior knowledge or “start from where
you or someone else left off”• Often not usable by non-technical personnel• Matching policies with technologies (e.g., National
Driver’s License DB)
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Intelligent, real-time matching
• Recognize threats by correlating across multiple databases / sources – “information fusion”
• Matches will often be approximate
• Human analysts can do further analysis (esp. if the number of alerts can be made small, but high-quality)
• Trade-off between sensitivity (TP/(TP+FN)) and specificity (TN/(TN+FP))
• Many homeland security applications – including financial security
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Finding the Best Fit
Query (range or fit)
Exact fits
Close fit
Close fit
Out of range
Out of range
Close matches are key!
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Context-Sensitive Fit
Price data Keyed data
Value determines distance
1 0 1
1 0 3
2 0 1
1 0 1
1 0 3
2 0 1
NearestNearest
Distance due to:- Keying adjacent digit- Skipped digit- Swapped digits
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The role of information
PersonalConfidential &
ProprietaryInformation
Security “Black Box”
InvestigationIndicated
Information Repository
IntelligentMatching
Combinations ofCharacteristics under Suspicion
Real-time Events
Conditions &Environment
PersonalConfidential &
ProprietaryInformation
DetectionPerformance
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Finer-grained Detection
Existing DetectionSmall Security Data Records• asdfkjlkj• askldfj;lkaj• lkjlkasdjf• lkjasdfk• akkjfdjk
CoarseSecurity Filter
FineSecurity Filter
Large Security Data Records• asdfkjlkj• askldfj;lkaj• lkjlkasdjf• kjasdfk• akkjfdjk• asdfkjlkj• askldfj;lkaj• lkjlkasdjf• lkjasdfk• akkjfdjk
Improved Detection
Investigate Suspects
Investigate Suspects
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Scenario Act 1
• Four transactions out of hundreds of millions:
• First transaction triggers additional automated queries• Secondary queries find other trans. and alert analyst• Analyst sets up additional queries monitoring for any
news involving Kahlil Binlasi or any suspicious activity correlated with Binlasi
Date Amount Payer Location Payee Location
8/20/02 $23,488 Lugano Ahmed Taleb Trenton8/21/02 $36,769 Zurich Jofar Khadem Newark8/22/02 $20,000 Ahmed Taleb Trenton Khalil Benlasi St Paul8/22/02 $30,000 Jofar Khadem Newark Kahlil Binlasi St Paul
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Scenario Act 2
• Police blotter story in 10/15/02 in local paper of Pine City, MN: Kalil Binlassi stopped with broken tail light, detained because he “acted suspicious”, and released.
• 10/22/02, news story about theft of explosives in Sandstone, MN, involving car of same model as Binlasi’s
• Analyst is alerted both times and on second story passes concerns to FBI who start direct surveillance, leading to eventual arrest.
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Intelligent Matching Technology
User Interface
Integration
Analytics
Notification Agent
s
• Best-of-breed component, open architecture, J2EE compliant
• Proprietary matching algorithms enable real-time, efficient matching of complex information
• Ultra-high performance - 100’s of complex matches per second
• Linearly scalable (in terms of both velocity and complexity)
• Large number of attributes
iXIntelligent Matching
Engine
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Key Innovations
Identifies and ranks based on “fit” with criteria
Immediately recognizes and acts on changes in the dataset
with persistent queries
Defines “fit” or nearness uniquely for each field type
Acts in real-time and linearly scalable
Intelligent Matching
• Simplifies data definition• “See” through imperfect data• Creates attraction• Matches all data types
• Armed to act fast & immediately when an event occurs• Observes all data that passes through
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Intelligent Matching Engine
Queries Records
Matcher
Data network management
Field algorithms
Config urator
Logging
API
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Intelligent Matching: Technology Environment (J2EE)
Presentation (Web/fat client)
Business Logic
Data Legacy Systems
J2EE Application
RMI calls
Persistent Storage
iX Server EJB
iX Management Interface
Matcher EJB
JDBC JCA
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Intelligent Matching: Technology Environment (Web Services)
Presentation (Web/fat client)
Business Logic
Data Legacy Systems
Application
SOAP calls
iX Server
iX Management Interface
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Demo
Financial security realm
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Summary
• Important policy issues surround the privacy / security spectrum– How do we increase security without diminishing privacy?– Is more information better; who has access to the
information?– Appropriate and inappropriate uses of information.
• New technologies for new challenges
• Data overload (making sense of it is like trying to drink from a fire hydrant)
• Intelligent matching with imperfect data is a key technology (that can be combined with improved feature detection and multiple-classifier algorithms)