CiteULike | Further Information

• {"link"=>{"url"=>"http://www.citeulike.org/user/Zhiyuwan/article/13568971"}, "post_time"=>"2015-04-03 06:53:56", "tag"=>["data", "game", "privacy", "theory"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0120592"}, "username"=>"Zhiyuwan", "article_id"=>"13568971"}

Mendeley | Further Information

{"title"=>"A game theoretic framework for analyzing re-identification risk", "type"=>"journal", "authors"=>[{"first_name"=>"Zhiyu", "last_name"=>"Wan", "scopus_author_id"=>"56581176600"}, {"first_name"=>"Yevgeniy", "last_name"=>"Vorobeychik", "scopus_author_id"=>"8913948800"}, {"first_name"=>"Weiyi", "last_name"=>"Xia", "scopus_author_id"=>"55620595300"}, {"first_name"=>"Ellen Wright", "last_name"=>"Clayton", "scopus_author_id"=>"7102432130"}, {"first_name"=>"Murat", "last_name"=>"Kantarcioglu", "scopus_author_id"=>"23135116900"}, {"first_name"=>"Ranjit", "last_name"=>"Ganta", "scopus_author_id"=>"8878053700"}, {"first_name"=>"Raymond", "last_name"=>"Heatherly", "scopus_author_id"=>"35096388000"}, {"first_name"=>"Bradley A.", "last_name"=>"Malin", "scopus_author_id"=>"6602093073"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84926339039", "sgr"=>"84926339039", "issn"=>"19326203", "isbn"=>"10.1371/journal.pone.0120592", "pmid"=>"25807380", "doi"=>"10.1371/journal.pone.0120592", "pui"=>"603466577"}, "id"=>"4efb4de0-9468-3eb5-8ce0-4838fc8ac54f", "abstract"=>"Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.", "link"=>"http://www.mendeley.com/research/game-theoretic-framework-analyzing-reidentification-risk-1", "reader_count"=>40, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Ph. D. Student"=>14, "Other"=>2, "Student > Master"=>9, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Ph. D. Student"=>14, "Other"=>2, "Student > Master"=>9, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>4, "Agricultural and Biological Sciences"=>3, "Neuroscience"=>2, "Chemical Engineering"=>1, "Social Sciences"=>3, "Computer Science"=>18}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>18}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>5}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Austria"=>1, "United States"=>1, "Luxembourg"=>1}, "group_count"=>0}

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CrossRef

• http://doi.org/10.2200/S00724ED1V01Y201607SPT017
• http://doi.org/10.1109/TKDE.2016.2628180
• http://doi.org/10.1177/2515245917747656
• http://doi.org/10.1016/j.ajhg.2016.12.002
• http://doi.org/10.1016/j.ijmedinf.2019.03.006
• http://doi.org/10.1186/s12911-020-1041-3
• http://doi.org/10.1109/JBHI.2017.2676880
• http://doi.org/10.1002/spe.2812
• http://doi.org/10.1016/j.jbi.2016.03.019
• http://doi.org/10.1016/j.techsoc.2018.03.005

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Article Coverage Curated

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Journal Comments | Further Information

• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/318db0ad-d034-41e9-91a5-cd4b0f7dd46b", "title"=>"Mode question: Is the publisher's loss (attacker's gain) modeled correctly?", "body"=>"I wonder if the publisher's re-ID loss, L, and the related assumption that the attacker's gain is equivalent to the publisher's loss, are appropriate model assumptions? These seem to be based upon the assumption that the attacker's gain (equivalently, by assumption, publisher's loss) is a one time gain(loss), but this may not be correct. Because these are information, not physical products, once the record has been re-ID'ed, the potential loss is potentially extremely large, and not static, because the same information could be sold by the attacker to any number of third parties as new calls arise for this information. As these assumptions are essential controlling parameters in the model, it seems important to analyze these assumptions carefully.", "isRemoved"=>false, "created"=>"2015-04-02T19:18:32Z", "lastModified"=>"2015-04-02T19:18:32Z", "creator"=>{"userId"=>"81771"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0120592", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2015-04-02T19:18:32Z", "replies"=>[]}

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• {"month"=>"6", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"32"}
• {"month"=>"7", "year"=>"2019", "pdf_views"=>"26", "xml_views"=>"0", "html_views"=>"35"}
• {"month"=>"8", "year"=>"2019", "pdf_views"=>"25", "xml_views"=>"0", "html_views"=>"19"}
• {"month"=>"9", "year"=>"2019", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"15"}
• {"month"=>"10", "year"=>"2019", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"37"}
• {"month"=>"11", "year"=>"2019", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"24"}
• {"month"=>"12", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"16"}
• {"month"=>"1", "year"=>"2020", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"19"}
• {"month"=>"2", "year"=>"2020", "pdf_views"=>"17", "xml_views"=>"1", "html_views"=>"28"}
• {"month"=>"3", "year"=>"2020", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"29"}
• {"month"=>"4", "year"=>"2020", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"21"}
• {"month"=>"5", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"11"}
• {"month"=>"6", "year"=>"2020", "pdf_views"=>"13", "xml_views"=>"1", "html_views"=>"22"}
• {"month"=>"7", "year"=>"2020", "pdf_views"=>"25", "xml_views"=>"0", "html_views"=>"31"}
• {"month"=>"8", "year"=>"2020", "pdf_views"=>"21", "xml_views"=>"3", "html_views"=>"41"}
• {"month"=>"9", "year"=>"2020", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"23"}
• {"month"=>"10", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"13"}
• {"month"=>"11", "year"=>"2020", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"34"}
• {"month"=>"12", "year"=>"2020", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"21"}
• {"month"=>"1", "year"=>"2021", "pdf_views"=>"6", "xml_views"=>"1", "html_views"=>"26"}
• {"month"=>"2", "year"=>"2021", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"39"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1986561"], "description"=>"<p>Algorithm 1: Backward Induction Search (<i>BIS</i>) Algorithm.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354208, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t004", "stats"=>{"downloads"=>1, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Algorithm_1_Backward_Induction_Search_BIS_Algorithm_/1354208", "title"=>"Algorithm 1: Backward Induction Search (<i>BIS</i>) Algorithm.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986542"], "description"=>"<p>Payoffs for the record ⟨48, Asian, Female, 38363⟩ across all strategies.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354189, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g005", "stats"=>{"downloads"=>2, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Payoff_across_strategies_/1354189", "title"=>"Payoff across strategies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986559"], "description"=>"<p>A summary of the notation used in this work.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354206, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t002", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_summary_of_the_notation_used_in_this_work_/1354206", "title"=>"A summary of the notation used in this work.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986538"], "description"=>"<p>The illustrative lattice for a database with two attributes Race and Age.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354185, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g004", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lattice_for_two_attributes_/1354185", "title"=>"Lattice for two attributes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986555"], "description"=>"<p>Sensitivity of the average payoffs over the dataset to the change of the publisher’s benefit, the publisher’s loss, or the adversary’s cost in different de-identification scenarios. <i>V</i> corresponds to the benefit that the publisher receives by sharing the record in its original form. <i>L</i> corresponds to the publisher’s loss for one record due to a successful re-identification. <i>c</i> corresponds to the adversary’s cost to launch a re-identification attack towards one record.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354202, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g011", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivities_of_different_scenarios_to_Benefit_or_Loss_/1354202", "title"=>"Sensitivities of different scenarios to Benefit or Loss.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986536"], "description"=>"<p>The Domain Generation Hierarchy (DGH) for the attribute Race in the case study.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354183, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g003", "stats"=>{"downloads"=>3, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DGH_for_Race_/1354183", "title"=>"DGH for Race.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986565"], "description"=>"<p>BIS: Backward Induction Search. LBS: Lattice-Based Search. Payoff difference means the absolute difference of payoff for one record between a heuristic-driven approach and the baseline BIS approach.</p><p>A performance comparison of the de-identification game solving approaches.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354212, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t008", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_performance_comparison_of_the_de_identification_game_solving_approaches_/1354212", "title"=>"A performance comparison of the de-identification game solving approaches.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986564"], "description"=>"<p>SH: Safe Harbor. GI: Generalization Intensity.</p><p>A comparison of four de-identification policies for the case study on performance measures.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354211, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t007", "stats"=>{"downloads"=>2, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_four_de_identification_policies_for_the_case_study_on_performance_measures_/1354211", "title"=>"A comparison of four de-identification policies for the case study on performance measures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986566"], "description"=>"<div><p>Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.</p></div>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354213, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592", "stats"=>{"downloads"=>14, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Game_Theoretic_Framework_for_Analyzing_Re_Identification_Risk_/1354213", "title"=>"A Game Theoretic Framework for Analyzing Re-Identification Risk", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986563"], "description"=>"<p>Recent notable HIPAA breach violation cases as reported by the U.S. Department of Health and Human Services.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354210, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t006", "stats"=>{"downloads"=>4, "page_views"=>85, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recent_notable_HIPAA_breach_violation_cases_as_reported_by_the_U_S_Department_of_Health_and_Human_Services_/1354210", "title"=>"Recent notable HIPAA breach violation cases as reported by the U.S. Department of Health and Human Services.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986545"], "description"=>"<p>Distributions of the publisher’s payoff differences (left) and the adversary’s payoff differences (right) between games and HIPAA Safe Harbor (SH).</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354192, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g006", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histogram_of_Payoff_Differences_/1354192", "title"=>"Histogram of Payoff Differences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986558"], "description"=>"<p>Notice that the policy requires the removal of explicit identifiers (e.g., the names of the corresponding individual or of their relatives, employers, or household members), quasi-identifiers (e.g., attributes that could potentially be linked to identify them, such as demographics), and unique codes (e.g., medical record numbers).</p><p>The attributes removed, or generalized, to satisfy the HIPAA Safe Harbor policy.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354205, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.t001", "stats"=>{"downloads"=>5, "page_views"=>132, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_attributes_removed_or_generalized_to_satisfy_the_HIPAA_Safe_Harbor_policy_/1354205", "title"=>"The attributes removed, or generalized, to satisfy the HIPAA Safe Harbor policy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986557"], "description"=>"<p>A comparison of the accuracy of the LBS game solving heuristic. <i>V</i> corresponds to the benefit that the publisher receives by sharing the record in its original form. <i>L</i> corresponds to the publisher’s loss for one record due to a successful re-identification. <i>c</i> corresponds to the adversary’s cost to launch a re-identification attack towards one record.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354204, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g012", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_Lattice_based_Search_/1354204", "title"=>"Accuracy of Lattice-based Search.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986550"], "description"=>"<p>Resulting strategies and payoffs for ⟨19, Black or African American, Male, 37208⟩ in the <i>Basic Game</i>. GI stands for the generalization intensity. <i>V</i> corresponds to the benefit that the publisher receives by sharing the record in its original form. <i>L</i> corresponds to the publisher’s loss for one record due to a successful re-identification.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354197, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g008", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_solution_1_in_Basic_Game_/1354197", "title"=>"Example solution 1 in Basic Game.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986553"], "description"=>"<p>Sensitivity of the average payoffs and strategies over the dataset to the changes of the publisher’s benefit and the publisher’s loss in the <i>Basic Game</i>. GI stands for the generalization intensity. <i>V</i> corresponds to the benefit that the publisher receives by sharing the record in its original form. <i>L</i> corresponds to the publisher’s loss for one record due to a successful re-identification.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354200, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g010", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_of_Basic_Game_to_Benefit_and_Loss_/1354200", "title"=>"Sensitivity of Basic Game to Benefit and Loss.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986534"], "description"=>"<p>The Domain Generation Hierarchy (DGH) for the attribute Age in the case study.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354181, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DGH_for_Age_/1354181", "title"=>"DGH for Age.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
• {"files"=>["https://ndownloader.figshare.com/files/1986551"], "description"=>"<p>Resulting strategies and payoffs for ⟨62, White, Female, 37014⟩ in the <i>Basic Game</i>. GI stands for the generalization intensity. <i>V</i> corresponds to the benefit that the publisher receives by sharing the record in its original form. <i>L</i> corresponds to the publisher’s loss for one record due to a successful re-identification.</p>", "links"=>[], "tags"=>["case study", "health Insurance Portability", "Accountability Act", "game theoretic framework", "share data", "grant funding dollar amounts", "sensitivity analysis", "organizations aim", "demonstrations show", "gain", "policy decisions", "privacy rules", "investigations focus", "player losses", "U.S", "hipaa"], "article_id"=>1354198, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Zhiyu Wan", "Yevgeniy Vorobeychik", "Weiyi Xia", "Ellen Wright Clayton", "Murat Kantarcioglu", "Ranjit Ganta", "Raymond Heatherly", "Bradley A. Malin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120592.g009", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_solution_2_in_Basic_Game_/1354198", "title"=>"Example solution 2 in Basic Game.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
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• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
• {"unique-ip"=>"10", "full-text"=>"12", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"9"}
• {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"10", "full-text"=>"7", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
• {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime