Yahtzee: An Anonymized Group Level Matching Procedure
Publication Date
February 05, 2013
Journal
PLOS ONE
Authors
Jason J. Jones, Robert M. Bond, Christopher J. Fariss, Jaime E. Settle, et al
Volume
8
Issue
2
Pages
e55760
DOI
https://dx.plos.org/10.1371/journal.pone.0055760
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0055760
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23441156
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3564933
Europe PMC
http://europepmc.org/abstract/MED/23441156
Web of Science
000314692800055
Scopus
84873507270
Mendeley
http://www.mendeley.com/research/yahtzee-anonymized-group-level-matching-procedure
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/495462"], "description"=>"<p>We categorized all friendships in our sample by decile, ranking them from lowest to highest percent of interactions. Each decile is a separate sample of friendship dyads. For example, decile 1 contains all friends at the 0th percentile of interaction to the 10th percentile while decile 2 contains all friends at the 11th percentile of interaction to the 20th, and so on. Interactions include actions on Facebook that could be directed from one user to another and include: comment, like, message, poke, wall post, tag or chat. These correlations exist well outside of simulated null distributions. 95% confidence intervals are displayed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone-0055760-t004\" target=\"_blank\">Table 4</a>.</p>", "links"=>[], "tags"=>["validated", "dyad", "months"], "article_id"=>165985, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g006", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_correlation_between_friends_validated_voting_behavior_based_on_the_proportion_of_interaction_between_the_dyad_in_the_three_months_prior_to_the_election_/165985", "title"=>"The correlation between friends' validated voting behavior based on the proportion of interaction between the dyad in the three months prior to the election.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:39:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/495594"], "description"=>"<p> is the number of draws necessary to reach the desired level of accuracy for the more frequent behavioral type and is the number of additional draws necessary to reach the desired level of accuracy for the less frequent behavioral type.</p>", "links"=>[], "tags"=>["computer science"], "article_id"=>166116, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.t002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_Draws_/166116", "title"=>"Number of Draws.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:41:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/495622"], "description"=>"<p>The estimated correlation between a user's validated turnout and the validated turnout of her friends by decile of user friend interactions. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone-0055760-g005\" target=\"_blank\">Figure 5</a> for a visualization of this relationship. To compare the observed values to what is possible due to chance, we keep the network topology fixed and then randomly permute the voting behavior of friends. We repeat this procedure 1,000 times and measure the correlation. The simulated correlation values generate a theoretical null distribution for the correlation which we would expect due to chance. The Null low and Null high columns display the 95% confidence interval of this null distribution. Note that the observed correlations exist well outside the null distributions.</p>", "links"=>[], "tags"=>["computer science"], "article_id"=>166145, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_/166145", "title"=>"Correlation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:42:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/495538"], "description"=>"<p>Example hash of first name, last name and date of birth. The last 7 digits of the SHA-256 hash value are kept and the rest of the hash discarded because of memory limitations. The 7 digit hash is a numeric hexadecimal value. For step 1, each round of the Yahtzee procedure begins with the hashing of the datasets using a new salt. The “salt” allows us to generate multiple hashes without getting the same hash every round. Next, the hash is divided by the value , where is the number of individuals in the dataset and was chosen arbitrarily. The remainder of this calculation is recorded as the group ID. Records are then placed into groups of various sizes based on this group ID. On average the groups should contain records. Next the frequency of some behavior of interest - in our case voting - is recorded for each group ID. In subsequent steps, a group ID is generated using the identical process on a second dataset. In the second dataset, the frequency of the behavior of interest is assigned to each record based on its group ID. In some cases, the same record is in both datasets, and its contribution to the value assigned to the group in the origin dataset will be transferred to the group in the destination dataset. However, individual records are never matched. We can be sure that identical records in both datasets will be assigned the same group ID, but we can never be sure for any one record if a true match exists in the other dataset or just records that hash to values with the same remainder after dividing by .</p>", "links"=>[], "tags"=>["computer science"], "article_id"=>166058, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hash_Example_/166058", "title"=>"Hash Example.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:40:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/495414"], "description"=>"<p>The dark line represents the turnout rate by age of the matched sample of Facebook users. Each gray line represents the turnout rate by age of a state voter record. The results show that users on Facebook exhibit the same pattern of turnout with respect to age as the populations of each state.</p>", "links"=>[], "tags"=>["matched", "users", "turned"], "article_id"=>165932, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_proportion_of_matched_users_who_turned_out_to_vote_by_age_/165932", "title"=>"The proportion of matched users who turned out to vote by age.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:38:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/482571"], "description"=>"<div><p>Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being “leaked” or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.</p> </div>", "links"=>[], "tags"=>["anonymized", "matching", "procedure"], "article_id"=>155856, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760", "stats"=>{"downloads"=>4, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Yahtzee_An_Anonymized_Group_Level_Matching_Procedure__/155856", "title"=>"Yahtzee: An Anonymized Group Level Matching Procedure", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:37:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/494974"], "description"=>"<p>In step 2 the group ID is determined for all groups where in the origin dataset and then matched to the same group ID from the destination group-level dataset. Notice that the hashing procedure and group aggregation is the same in both datsets except we keep all groups in the destination dataset, regardless of size. This is so because we only need to know the group size from the origin dataset to make predictions about the behavior in the destination dataset. Once the group-level datasets are matched by the group ID, the group-level information is stored and the process is repeated times. In step 3 the group level data is sent to the holder of the destination dataset so that the group level values can be assigned to the individual observations based on the same hashes used in the construction of the groups during each of the Yahtzee rounds. Once the destination dataset has acquired a sufficient number of group level values (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone-0055760-g002\" target=\"_blank\">Figure 2</a> for information on determining the value of ) it is possible to then use the combined information to predict the behavior of each individual, which is step 4 of the Yahtzee procedure. For our application, using <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone.0055760.e053\" target=\"_blank\">equations 4</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone.0055760.e054\" target=\"_blank\">5</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055760#pone.0055760.e055\" target=\"_blank\">6</a> above, it is possible to predict if the individual is unregistered, a voter or an abstainer. Finally, it is worth repeating that only the group-level data is passed from the origin to the destination dataset. See the Pseudocode for additional information.</p>", "links"=>[], "tags"=>["yahtzee", "begins", "hashing", "datasets"], "article_id"=>165490, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Step_1_of_the_Yahtzee_procedure_begins_with_the_hashing_of_the_datasets_using_a_new_salt_See_Table_1_above_/165490", "title"=>"Step 1 of the Yahtzee procedure begins with the hashing of the datasets using a new salt (See Table 1 above).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:31:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/495269"], "description"=>"<p>Note that the abbreviation for Kansas is repositioned slightly so that it does not overlap with the abbreviation for Florida. The results show that the Yahtzee procedure produces about the same overall turnout rate for each state as that shown in the official voter record.</p>", "links"=>[], "tags"=>["matched", "users", "turned", "compared", "turnout"], "article_id"=>165785, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_proportion_of_matched_users_who_turned_out_to_vote_compared_to_the_overall_turnout_rate_by_state_/165785", "title"=>"The proportion of matched users who turned out to vote compared to the overall turnout rate by state.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:36:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/495111"], "description"=>"<p>The dark line represents the accuracy rate for true participators. The light line represents the accuracy rate for true abstainers. Accuracy increases for both categories as observations for each individual are obtained from the Yahtzee procedure. Note that the less frequent of the two behaviors requires fewer observations for classification than the more frequent behavior. is the number of observations per person necessary to achieve a given level of accuracy for the less frequent behavior and is the number of observations necessary to achieve a given level of accuracy for the more frequent behavior.</p>", "links"=>[], "tags"=>["predictions", "rates", "held", "simulation", "matching"], "article_id"=>165627, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_proportion_of_correct_predictions_for_participation_rates_of_30_45_55_and_70_the_match_rate_is_held_constant_at_30_in_all_four_figures_from_a_simulation_of_the_matching_procedure_/165627", "title"=>"The proportion of correct predictions for participation rates of 30%, 45%, 55%, and 70% (the match rate is held constant at 30% in all four figures) from a simulation of the matching procedure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:33:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/495347"], "description"=>"<p>This figure helps to explain why match rates are lower for Facebook users who tend to be younger and more difficult to match than the average registered voter.</p>", "links"=>[], "tags"=>["facebook", "users", "matched", "validated", "largest", "20", "years"], "article_id"=>165853, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_proportion_of_Facebook_users_that_were_matched_to_the_validated_voting_record_by_age_and_each_age_group_s_proportion_of_the_largest_age_group_those_20_years_of_age_at_the_time_of_the_election_/165853", "title"=>"The proportion of Facebook users that were matched to the validated voting record by age and each age group's proportion of the largest age group (those 20 years of age at the time of the election).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:37:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/495558"], "description"=>"<p>Yahtzee classifier results from 1000 randomly selected Facebook users from each state. Each user was given a classification based on the Yahtzee process: “Abs” Abstainer, “Vot” Voter, “NM” Not Matched. The conditional probabilities are calculated as the probability of observing a true behavior conditional on the Yahtzee classification. The 95% confidence intervals are for the null distribution of 95% accuracy in the classification, calculated from a binomial distribution with the same number of draws in each category. In total, 22 of the 26 tests fall within these intervals, suggesting that deviations from 95% accuracy are due to sampling variation, and for a large sample the procedure will generate the desired level of accuracy.</p>", "links"=>[], "tags"=>["computer science"], "article_id"=>166079, "categories"=>["Information And Computing Sciences"], "users"=>["Jason J. Jones", "Robert M. Bond", "Christopher J. Fariss", "Jaime E. Settle", "Adam D. I. Kramer", "Cameron Marlow", "James H. Fowler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055760.t003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_Draws_/166079", "title"=>"Number of Draws.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:41:19"}

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Relative Metric

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