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Computational Cybersecurity in
Compromised Environments (C3E )
Jan 14, 2014

Computer
Human
Interaction:
Mobility
Privacy
...
•

Bandwidth

Time

©2014 Carnegie Mellon University : 2

Computing Trends
Bandwidth

•

Storage

•

Time

©2014 Carnegie Mellon University : 3

Computing Trends
Bandwidth

•

Storage

•

Computing Power

•

Time

©2014 Carnegie Mellon University : 4

Computing Trends
Bandwidth

•

Storage

•

Computing Power

•

Information

•

Time

©2014 Carnegie Mellon University : 5

Computing Trends
©2014 Carnegie Mellon University : 6
Cognitive Processing

•

Time

©2014 Carnegie Mellon University : 7

Human Capabilities
Cognitive Processing

•

Visual acuity

•

Time

©2014 Carnegie Mellon University : 8

Human Capabilities
Cognitive Processing

•

Visual acuity

•

Human bandwidth
…
•

Time

©2014 Carnegie Mellon University : 9

Human Capabili...
7
2
©2014 Carnegie Mellon University : 10
Evidence suggests it’s more like 4
©2014 Carnegie Mellon University : 11
©2014 Carnegie Mellon University : 12
1. Start out going Southwest on ELLSWORTH AVE
Towards BROADWAY by turning right.
2: Turn RIGHT onto BROADWAY.
3. Turn RIGH...
©2014 Carnegie Mellon University : 14
©2014 Carnegie Mellon University : 15

The Power of Visualization
1. Aesthetics and color really matter
2. Study what people are trying to do
3. InfoViz is also what you don’t show

©2014 ...
©2014 Carnegie Mellon University : 17

US Election 2004
©2014 Carnegie Mellon University : 18

InfoViz’s Can Show and Hide
Info
©2014 Carnegie Mellon University : 19

All Viz’s Show and Hide Info
©2014 Carnegie Mellon University : 20

InfoViz’s Can Show and Hide
Info
©2014 Carnegie Mellon University : 21

All Viz’s Show and Hide Info
1.
2.
3.
4.

Aesthetics and color really matter
Study what people are trying to do
Infoviz is also what you don’t show
All...
©2014 Carnegie Mellon University : 23

London Underground Map 1990s
©2014 Carnegie Mellon University : 24

Visualization of DNA

by Ben Fry
©2014 Carnegie Mellon University : 25

Visualization of the Internet
©2014 Carnegie Mellon University : 26

Earlier Conceptions of the Net
©2014 Carnegie Mellon University : 27
1.
2.
3.
4.
5.

Aesthetics and color really matter
Study what people are trying to do
Infoviz is also what you don’t show
...
Example from Jeff Heer

©2014 Carnegie Mellon University : 29
©2014 Carnegie Mellon University : 30
©2014 Carnegie Mellon University : 31
Work by Jeff Heer
©2014 Carnegie Mellon University : 32
©2014 Carnegie Mellon University : 33

About 85 per cent of my
"thinking" time was spent
getting into a position to
think,...
©2014 Carnegie Mellon University : 34
1.
2.
3.
4.
5.

Aesthetics and color really matter
Study what people are trying to do
Infoviz is also what you don’t show
...
• Many Eyes (by IBM)

©2014 Carnegie Mellon University : 36

Collaborative Analysis?
©2014 Carnegie Mellon University : 37
©2014 Carnegie Mellon University : 38
• Pay Mturkers to
help find potential
problems with
smartphone apps

©2014 Carnegie Mellon University : 39

CrowdScanner

...
User can specify exemplars of a group
Belief Propagation to find more nodes

©2014 Carnegie Mellon University : 40

Combin...
• Mixed-initiative: Human + Machine

I feel like I have a
“partnership with the machine”
• Builds a highly personalized la...
• Considering visualizations
1.
2.
3.
4.
5.

Aesthetics and color really matter
Study what people are trying to do
Infoviz...
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C3E talk on Navigating Cyberspace, January 2014

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A brief 15 minute overview of what does and doesn't work in information visualization, plus a brief discussion of how to address issues of scale (collaborative analysis, crowdsourcing, machine learning)

Published in: Technology, Economy & Finance
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C3E talk on Navigating Cyberspace, January 2014

  1. 1. Computational Cybersecurity in Compromised Environments (C3E ) Jan 14, 2014 Computer Human Interaction: Mobility Privacy Security ©2009 Carnegie Mellon University : 1 Making Sense of Navigating Cyberspace Jason Hong
  2. 2. • Bandwidth Time ©2014 Carnegie Mellon University : 2 Computing Trends
  3. 3. Bandwidth • Storage • Time ©2014 Carnegie Mellon University : 3 Computing Trends
  4. 4. Bandwidth • Storage • Computing Power • Time ©2014 Carnegie Mellon University : 4 Computing Trends
  5. 5. Bandwidth • Storage • Computing Power • Information • Time ©2014 Carnegie Mellon University : 5 Computing Trends
  6. 6. ©2014 Carnegie Mellon University : 6
  7. 7. Cognitive Processing • Time ©2014 Carnegie Mellon University : 7 Human Capabilities
  8. 8. Cognitive Processing • Visual acuity • Time ©2014 Carnegie Mellon University : 8 Human Capabilities
  9. 9. Cognitive Processing • Visual acuity • Human bandwidth … • Time ©2014 Carnegie Mellon University : 9 Human Capabilities
  10. 10. 7 2 ©2014 Carnegie Mellon University : 10
  11. 11. Evidence suggests it’s more like 4 ©2014 Carnegie Mellon University : 11
  12. 12. ©2014 Carnegie Mellon University : 12
  13. 13. 1. Start out going Southwest on ELLSWORTH AVE Towards BROADWAY by turning right. 2: Turn RIGHT onto BROADWAY. 3. Turn RIGHT onto QUINCY ST. 4. Turn LEFT onto CAMBRIDGE ST. 5. Turn SLIGHT RIGHT onto MASSACHUSETTS AVE. 6. Turn RIGHT onto RUSSELL ST. ©2014 Carnegie Mellon University : 13 The Power of Visualization
  14. 14. ©2014 Carnegie Mellon University : 14
  15. 15. ©2014 Carnegie Mellon University : 15 The Power of Visualization
  16. 16. 1. Aesthetics and color really matter 2. Study what people are trying to do 3. InfoViz is also what you don’t show ©2014 Carnegie Mellon University : 16 Some Lessons
  17. 17. ©2014 Carnegie Mellon University : 17 US Election 2004
  18. 18. ©2014 Carnegie Mellon University : 18 InfoViz’s Can Show and Hide Info
  19. 19. ©2014 Carnegie Mellon University : 19 All Viz’s Show and Hide Info
  20. 20. ©2014 Carnegie Mellon University : 20 InfoViz’s Can Show and Hide Info
  21. 21. ©2014 Carnegie Mellon University : 21 All Viz’s Show and Hide Info
  22. 22. 1. 2. 3. 4. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases ©2014 Carnegie Mellon University : 22 Some Lessons
  23. 23. ©2014 Carnegie Mellon University : 23 London Underground Map 1990s
  24. 24. ©2014 Carnegie Mellon University : 24 Visualization of DNA by Ben Fry
  25. 25. ©2014 Carnegie Mellon University : 25 Visualization of the Internet
  26. 26. ©2014 Carnegie Mellon University : 26 Earlier Conceptions of the Net
  27. 27. ©2014 Carnegie Mellon University : 27
  28. 28. 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models ©2014 Carnegie Mellon University : 28 Some Lessons
  29. 29. Example from Jeff Heer ©2014 Carnegie Mellon University : 29
  30. 30. ©2014 Carnegie Mellon University : 30
  31. 31. ©2014 Carnegie Mellon University : 31
  32. 32. Work by Jeff Heer ©2014 Carnegie Mellon University : 32
  33. 33. ©2014 Carnegie Mellon University : 33 About 85 per cent of my "thinking" time was spent getting into a position to think, to make a decision… Much more time went into finding or obtaining information than into digesting it… When the graphs were finished, the relations were obvious at once, but the plotting had to be done in order to make them so. - J.C.R. Licklider, 1960
  34. 34. ©2014 Carnegie Mellon University : 34
  35. 35. 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models 6. Viz just one part of toolchain ©2014 Carnegie Mellon University : 35 Some Lessons
  36. 36. • Many Eyes (by IBM) ©2014 Carnegie Mellon University : 36 Collaborative Analysis?
  37. 37. ©2014 Carnegie Mellon University : 37
  38. 38. ©2014 Carnegie Mellon University : 38
  39. 39. • Pay Mturkers to help find potential problems with smartphone apps ©2014 Carnegie Mellon University : 39 CrowdScanner 95% users were surprised this app sent their approximate location to mobile ads providers. 95% users were surprised this app sent their phone’s unique ID to mobile ads providers. 90% users were surprised this app sent their precise location to mobile ads providers. 0% users were surprised this app can control camera flashlight.
  40. 40. User can specify exemplars of a group Belief Propagation to find more nodes ©2014 Carnegie Mellon University : 40 Combine Data Mining + Viz
  41. 41. • Mixed-initiative: Human + Machine I feel like I have a “partnership with the machine” • Builds a highly personalized landscape (unlike automatic methods) ©2014 Carnegie Mellon University : 41 Apolo’s Key Contributions
  42. 42. • Considering visualizations 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models 6. Viz just one part of toolchain • Ongoing research – Collaborative analysis – Machine learning + infoviz ©2014 Carnegie Mellon University : 42 Summary

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