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Emerging Technologies, Homeland Security and the PrivacySecurity Tradeoff Dr' Phil Hayes

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Dr. Phil Hayes & Dr. Ganesh Mani. May 29, 2002. 2. Proprietary and Confidential. Agenda ... The role of the Internet is broadly changing the semantics of privacy ... – PowerPoint PPT presentation

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Title: Emerging Technologies, Homeland Security and the PrivacySecurity Tradeoff Dr' Phil Hayes


1
Emerging Technologies, Homeland Security and the
Privacy/Security Trade-offDr. Phil Hayes Dr.
Ganesh Mani
  • May 29, 2002

2
Agenda
  • Background
  • Current Technologies and their Limitations
  • New / Emerging Technologies (esp. Intelligent
    Matching)
  • Summary and Conclusions

3
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 somebodys 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

4
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.

5
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.)

6
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

7
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 Drivers License DB)

8
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/(TPFN)) and
    specificity (TN/(TNFP))
  • Many homeland security applications including
    financial security

9
Finding the Best Fit
Close fit
Out of range
Close fit
Query (range or fit)
Exact fits
Out of range
Close matches are key!
10
Context-Sensitive Fit
Price data
Keyed data
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1
Nearest
Nearest
1
0
3
1
0
3
2
0
1
2
0
1
Value determines distance
  • Distance due to
  • Keying adjacent digit
  • Skipped digit
  • Swapped digits

11
The role of information
Security Black Box
Personal Confidential Proprietary Information
Personal Confidential Proprietary Information
Information Repository
Intelligent Matching
Real-time Events
Investigation Indicated
Combinations of Characteristics under Suspicion
Conditions Environment
Detection Performance
12
Finer-grained Detection
Existing Detection
  • Small Security Data Records
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Suspects
Investigate
Coarse Security Filter
Improved Detection
  • Large Security Data Records
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FineSecurity Filter
Investigate
Suspects
13
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

14
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
    Binlasis
  • Analyst is alerted both times and on second story
    passes concerns to FBI who start direct
    surveillance, leading to eventual arrest.

15
Intelligent Matching Technology
  • Proprietary matching algorithms enable real-time,
    efficient matching of complex information
  • Ultra-high performance - 100s of complex matches
    per second

iXIntelligent Matching Engine
  • Large number of attributes
  • Linearly scalable (in terms of both velocity and
    complexity)
  • Best-of-breed component, open architecture, J2EE
    compliant

16
Key Innovations
Identifies and ranks based on fit with criteria
  • Simplifies data definition
  • See through imperfect data
  • Creates attraction
  • Matches all data types

Defines fit or nearness uniquely for each field
type
Acts in real-time and linearly scalable
Intelligent Matching
Immediately recognizes and acts on changes in the
dataset with persistent queries
  • Armed to act fast immediately
  • when an event occurs
  • Observes all data that
  • passes through

17
Intelligent Matching Engine
18
Intelligent Matching Technology Environment
(J2EE)
19
Intelligent Matching Technology Environment (Web
Services)
20
Demo
  • Financial security realm

21
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)
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