Sybil Attacks Against Mobile Users
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Sybil Attacks Against Mobile Users

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"Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue". Presentation at INFOCOM 2010 of this paper

"Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue". Presentation at INFOCOM 2010 of this paper
http://eprints.ucl.ac.uk/18812/1/18812.pdf

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  • U use EBAY. In my research
  • People in a train, with their phones, willing to share pics/videos That is because…
  • Fast!!!
  • [slow down] one the way back home they are willing to share pic/videos
  • [pause]
  • Too much content around. My phone doesn’t know from whom to download pixcs/videos One of the solutions is the proposal of my phd
  • For the last 3 years I’ve been thinking how to do that Thumbs up/ down -> ratings -> to aututomatically select reputable people And, for 3 years, I;’ve been thinking about just 2 things….
  • How to store And how to use Period. That’s what I was doing all day long for 3 years My dayhob paid by you guys was to dayfream and come up with ideas on how … My goal: [hand] is to presents two papers.
  • [pause] Le’ts start with the first problem

Sybil Attacks Against Mobile Users Presentation Transcript

  • 1. _ _
  • 2. I did my PhD @
  • 3. U niversity C ollege L ondon
  • 4. <PhD>
  • 5. Ratings on ...
  • 6. Ratings on phones
  • 7. Why ratings on mobiles?
  • 8. Situation : People exchange digital content
  • 9. People
  • 10.  
  • 11.  
  • 12.  
  • 13. The problem is ...
  • 14. drowning user (content overload) help! who will come to the rescue?
  • 15. Proposal: Accept content only from reputable people
  • 16. Use Store
  • 17. Use Store MobiRate [Ubicomp08]
  • 18. Use Store LDTP [ICDM07] MobiRate [Ubicomp08]
  • 19. MobID
  • 20. Collaborative applications: track trustworthiness of IDs
  • 21. one misbehaves & creates bogus identities (goes untraceable)
  • 22. Traditional Solutions
  • 23. pki
  • 24. p2p socnet fast mixing & no churn
  • 25. 1. u’ve got friends: honest & key-enabled! 2. u don’t move randomly 3. homophily: P(honest  honest)=HIGH assumptions
  • 26. “ A meets B” @A: 1. B &co. into its network 2. rank B 3. accept/reject B
  • 27.  
  • 28.  
  • 29.  
  • 30.  
  • 31. device keeps 2 nets & reasons on them
  • 32.  
  • 33. 1. B &co. into networks 2. 2 ranks for B 3. accept/reject B 4. update networks` “ A meets B”
  • 34. GoodRank BadRank B C D E F G L M N O P
  • 35. GoodRank BadRank B C D E F G L M N O P
  • 36. Does it work?
  • 37.  
  • 38.  
  • 39.  
  • 40.  
  • 41.  
  • 42. issue 1: social exclusion
  • 43. MobID + FriendSensing
  • 44. FriendSensing ((.))
  • 45.  
  • 46. Keep Bluetooth on (record who is around)
  • 47. Upload records
  • 48. People you may know
  • 49. People You May Know Forrest Gump Bambie Barack Obama Add to Friends Add to Friends Add to Friends
  • 50. MobID + FriendSensing
  • 51. If you are looking for Kinky boots issue 2
  • 52. whether you are …
  • 53. a woman…
  • 54. or a man a woman…
  • 55. Thorny problem:
  • 56. … or a woman… How to keep it secret
  • 57.