1. Anonymity and Privacy Issues --- re-identification
Yimeng Zhang
12/4/07

2. 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

3. Improper Use of Personal Information Online

4. Top Privacy Concerns

5. Remaining Anonymous

6. True Information Provide While Registering

7. Ability to Remain Anonymous

8. Importance of Controlling Personal Information

9. Specifying Who Can View Personal Information

10. 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

11. 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

12. Example of Re-identification
Voter register list of Massachusetts purchased with only 20$
Public by Group Insurance Commission of Massachusetts
87% of Population in US are likely to be uniquely identified based on only on Zip, Birth and Sex
Sweeney, 2002

13. Governor’s medical records!
The Rebus Form + =
Governor’s medical records!
From Frankowski, SIGIR06

14. Example of face identification
Without explicit identified profiles
With explicit identified profiles
Friendster
Facebook
Identity violation!
Face Recognizer
Gross and Acquisti, WPES 05

15. 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

16. 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

17. 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 p3

18. Research Questions Risks of dataset release Altering the dataset
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

19. Experiment Dataset: MovieLens
Dataset1: Movie Ratings
Users do not allow to reveal
Released for research use
“Anonymous Dataset”
Dataset2: Movies Reviews
Public

20. Feature of the dataset Both ratings and mentions follow a power law
Important feature for real world sparse relation space
Frankowski, SIGIR 06

21. Evaluation Measure Ratings Mentions Mentions by User t 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

22. 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

23. 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

24. 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

25. 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

26. Percent of users identified by different algorithms

27. 1-identification rate

28. 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

29. Dataset level Suppression
Do not work!

30. RQ3: Self Defence How can users protect their own privacy Suppression
Not to mention movies rated rarely
Misdirection
Mention items they have not rated

31. User Level Suppression
Do not work!

32. Misdirection
Works when user mention popular items

33. 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
Don’t reveal your privacies even if it seems to be anonymous