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Large Scale Threats to Data Anonymity Arvind Narayanan Joint work with Vitaly Shmatikov Kamalika Chaudhuri
Anonymity is not cryptography ,[object Object],[object Object],[object Object],[object Object],[object Object]
The curse of dimensionality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Auxiliary information ,[object Object],[object Object],[object Object],[object Object]
Two fallacies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interactive protocols ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sad realization #2 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Sweeney – linking (exact match) (Anonymous) (Non-anonymous) Hardly secret Probably not secret
Collaborative filtering: profiles ,[object Object],[object Object],[object Object],[object Object]
Movielens – fuzzy match ,[object Object],[object Object],[object Object],[object Object],[object Object]
Netflix – fuzzy match with noise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Distance to nearest neighbor
Netflix’s take on privacy Even if, for example, you knew  all  your own ratings and their dates you  probably  couldn’t identify them reliably in the data because only a small sample was included (less than  one-tenth  of our complete dataset) and that data was subject to  perturbation . Of course, since you know all  your own  ratings that really isn’t a privacy problem is it?  -- Netflix Prize FAQ
Example of deanonymization
Netflix – contributions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Netflix customers with distance < 0.15 ,[object Object],[object Object]
Recommenders: stronger attacks ,[object Object],[object Object],[object Object],[object Object],[object Object]
Social networks ,[object Object],[object Object],[object Object],[object Object]
Backstrom, Dwork, Kleinberg ,[object Object],[object Object],[object Object]
Narayanan, Chaudhuri ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Finding yourself ,[object Object],[object Object]
 
Propagation of node re-identification ,[object Object],[object Object],[object Object]
 
 
Propagation – implementation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Author identification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genome anonymity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genome and DNA databases ,[object Object],[object Object],[object Object]
Hapmap’s take on privacy The samples are anonymous with regard to individual identity. Samples cannot be connected to individuals, and no personal information is linked to any sample.  As an additional safeguard, more samples were collected from each population than were used, so no one knows whether any particular person's DNA is included in the study.
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genotype – phenotype mappings ,[object Object],[object Object],[object Object],[object Object],[object Object]
Underlying social network
Big picture ,[object Object],[object Object],[object Object],[object Object],[object Object]
Thanks for coming.
Current work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Anonymity

  • 1. Large Scale Threats to Data Anonymity Arvind Narayanan Joint work with Vitaly Shmatikov Kamalika Chaudhuri
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. Sweeney – linking (exact match) (Anonymous) (Non-anonymous) Hardly secret Probably not secret
  • 9.
  • 10.
  • 11.
  • 13. Netflix’s take on privacy Even if, for example, you knew all your own ratings and their dates you probably couldn’t identify them reliably in the data because only a small sample was included (less than one-tenth of our complete dataset) and that data was subject to perturbation . Of course, since you know all your own ratings that really isn’t a privacy problem is it? -- Netflix Prize FAQ
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  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.  
  • 23.
  • 24.  
  • 25.  
  • 26.
  • 27.
  • 28.
  • 29.
  • 30. Hapmap’s take on privacy The samples are anonymous with regard to individual identity. Samples cannot be connected to individuals, and no personal information is linked to any sample. As an additional safeguard, more samples were collected from each population than were used, so no one knows whether any particular person's DNA is included in the study.
  • 31.
  • 32.
  • 34.
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  • 37.