may 29, 2002

Proprietary and Confidential

Emerging Technologies, Homeland Security and the Privacy/Security Trade-off

Dr. Phil Hayes & Dr. Ganesh ManiMay 29, 2002

2 Proprietary and Confidential

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)