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May 29, 2002 PowerPoint Presentation
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May 29, 2002

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May 29, 2002

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  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 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

  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 Driver’s 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/(TP+FN)) and specificity (TN/(TN+FP)) • 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 1 0 1 1 0 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 • asdfkjlkj • askldfj;lkaj • lkjlkasdjf • lkjasdfk • akkjfdjk Suspects Investigate Coarse Security Filter Improved Detection • Large Security Data Records • asdfkjlkj • askldfj;lkaj • lkjlkasdjf • kjasdfk • akkjfdjk • asdfkjlkj • askldfj;lkaj • lkjlkasdjf • lkjasdfk • akkjfdjk 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 Binlasi’s • Analyst is alerted both times and on second story passes concerns to FBI who start direct surveillance, leading to eventual arrest.

  15. User Interface Analytics Notification Agents Integration Intelligent Matching Technology • Proprietary matching algorithms enable real-time, efficient matching of complex information • Ultra-high performance - 100’s 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)