Anonymity and Privacy Issues --- re-identification

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Anonymity and Privacy Issues--- re-identification

• Yimeng Zhang
• 12/4/07

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Index

• Views on Privacy of Social Media
• Overview of Re-identification
• You are What You Say Privacy Risks of Public
  Mentions, Frankowski et al. SIGIR06

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Improper Use of Personal Information Online
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Top Privacy Concerns
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Remaining Anonymous
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True Information Provide While Registering
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Ability to Remain Anonymous
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Importance of Controlling Personal Information
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Specifying Who Can ViewPersonal Information
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Conclusion

• Around 40 of people would like to remain
  anonymous on social media or social networking
  sites
• Most people provide their true personal
  information while registering
• Most people think it is important to have the
  control of personal information online

Re-identification Techniques can identify the
users of an anonymous dataset
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Privacy Loss through Re-identification

• Re-identification Linkage of datasets with
  explicit identifiers with datasets without
  explicit identifiers through common attributes
• Datasets without explicit identifiers
• Public data which are made anonymous by users
• Public data by research groups (after suitable
  anonymizing)
• Public data from government agencies (census)

People wish to keep private
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Example of Re-identification
Voter register list of Massachusetts purchased
with only 20
87 of Population in 1990. US are likely to be
uniquely identified based on only on Zip, Birth
and Sex
Sweeney, 2002
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The Rebus Form


Governors medical records!
From Frankowski, SIGIR06
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Example of face identification
Without explicit identified profiles
With explicit identified profiles
Friendster
Facebook
Identity violation!
Face Recognizer
Gross and Acquisti, WPES 05
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You Are What You Say Privacy Risks of Public
Mentions

• Dan Frankowski, Dan Cosley, Shilad Sen, Loren
  Terveen, John Riedl
• University of Minnesota
• SIGIR 2006

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Main Idea

• People can be identified by their preferences and
  what they talk about
• Reviews of books, movies, songs
• Mentions on forums or blogs
• Friend list on Facebook
• Wish or purchase list on Amazon
• Method for Re-identification
• Datasets are represented in Sparse Relation
  Spaces
• Re-identification can be done by matching two
  Sparse Relation Spaces

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Sparse Relation Space

• Relates people to items
• Sparse have few relationships recorded per
  person
• Dataset that can be represented in a Sparse
  Relation Space is vulnerable

i1 i2 i3
p1 X
p2 X
p3 X

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Research Questions

• Risks of dataset release
• What are the risks to user privacy when releasing
  a dataset
• Altering the dataset
• How can dataset owners alter the dataset to
  preserve user privacy
• Self defense
• How can users protect their own privacy

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Experiment Dataset MovieLens
Dataset1 Movie Ratings Users do not allow to
reveal Released for research use Anonymous
Dataset
Dataset2 Movies Reviews Public
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Feature of the dataset

• Both ratings and mentions follow a power law
• Important feature for real world sparse relation
  space

Frankowski, SIGIR 06
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Evaluation Measure
Mentions
Mentions by User t
Ratings
Re-identify Algorithm
Top k ratings users ranked by the likelihood they
are user t
K-identified t is in the k users returned by the
algorithm K-identification rate the fraction of
k-identified users
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Set Intersection Algorithm for Re-identification

• Likely list Users in the rating database who
  have rated every movie mentions by user t
• Problem
• Users mention movies but do not rate them

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TF-IDF Algorithm

• Mentions of a user vector of the movies the user
  mentioned
• Ratings of a user vector of the movies the user
  rated
• Likelihood TF-IDF cosine similarity

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Scoring Algorithm

• Scoring
• emphasize the mentions of rarely rated movies
• de-emphasize the number of ratings a user has

Score for one mention/movie of a user
Fraction of users who have not rated mention m
Score for a user Multiplication of scores for
all mentions of this user
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Scoring Algorithm with Ratings

• Suppose we have an magic analyzer which can guess
  the rating of a movie from the mention
• Eg. Using the context of that mention
• Algorithms
• ExactRating the analyzer can perfectly determine
  the rating
• FuzzingRaing the analyzer can guess the rating
  value within /-1

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Percent of users identified by different
algorithms
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1-identification rate
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RQ2 Altering the dataset

• How can dataset owners alter the dataset they
  release to preserve user privacy
• Data Suppression
• Algorithm Drop rarely rated movies
• Not big problem for industry, but harmful for
  research

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Dataset level Suppression
Do not work!
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RQ3 Self Defence

• How can users protect their own privacy
• Suppression
• Not to mention movies rated rarely
• Misdirection
• Mention items they have not rated

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User Level Suppression
Do not work!
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Misdirection
Works when user mention popular items
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Conclusion

• Simple data mining algorithms can identify the
  users who mention in a sparse relation space and
  think they are anonymous
• Use the algorithms eg. find paper reviewers
  (Future work of Frankowski)
• Privacy risks for users on Social Media sites
• Hard to preserve privacies
• Dont reveal your privacies even if it seems to
  be anonymous