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Anonymity and Privacy Issues --- re-identification Yimeng Zhang 12/4/07 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 Improper Use of Personal Information Online Top Privacy Concerns Remaining Anonymous True Information Provide While Registering Ability to Remain Anonymous Importance of Controlling Personal Information Specifying Who Can View Personal Information 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 Privacy Loss through Re-identification • Re-identification: Linkage of datasets with explicit identifiers with datasets without explicit identifiers through common attributes People wish to keep private • 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) Example of Re-identification Public by Group Insurance Commission of Massachusetts 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 The Rebus Form + = Governor’s medical records! From Frankowski, SIGIR06 Example of face identification With explicit identified profiles Without explicit identified profiles Friendster Facebook Identity violation! Face Recognizer Gross and Acquisti, WPES 05 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 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 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 p1 p2 p3 … i3 X X X … 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 Experiment Dataset: MovieLens Dataset1: Movie Ratings Users do not allow to reveal Released for research use “Anonymous Dataset” Dataset2: Movies Reviews Public Feature of the dataset Number of ratings of an item by percentile 60000 50000 40000 Number of ratings • Both ratings and mentions follow a power law • Important feature for real world sparse relation space 30000 20000 10000 0 0% 20% 40% 60% Item percentile 80% Frankowski, SIGIR 06 100% 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 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 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 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 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 Percent of users identified by different algorithms 1-identification rate 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 Dataset level Suppression Do not work! RQ3: Self Defence • How can users protect their own privacy • Suppression – Not to mention movies rated rarely • Misdirection – Mention items they have not rated User Level Suppression Do not work! Misdirection Works when user mention popular items 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