A Game Theoretic Framework for Analyzing Re-Identification Risk
Publication Date
March 25, 2015
Journal
PLOS ONE
Authors
Zhiyu Wan, Yevgeniy Vorobeychik, Weiyi Xia, Ellen Wright Clayton, et al
Volume
10
Issue
3
Pages
e0120592
DOI
http://doi.org/10.1371/journal.pone.0120592
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0120592
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25807380
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373733
Europe PMC
http://europepmc.org/abstract/MED/25807380
Web of Science
000351880000088
Scopus
84926339039
Mendeley
http://www.mendeley.com/research/game-theoretic-framework-analyzing-reidentification-risk-1
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Mendeley | Further Information

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Scopus | Further Information

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  • {"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"}
  • {"files"=>["https://ndownloader.figshare.com/files/1986533"], "description"=>"<p>An illustrative example of the re-identification game with four data sharing 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"=>1354180, "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.g001", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustrative_example_/1354180", "title"=>"An illustrative example.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1986562"], "description"=>"<p>Algorithm 2: Lattice-Based Search (<i>LBS</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"=>1354209, "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.t005", "stats"=>{"downloads"=>4, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Algorithm_2_Lattice_Based_Search_LBS_Algorithm_/1354209", "title"=>"Algorithm 2: Lattice-Based Search (<i>LBS</i>) Algorithm.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1986547"], "description"=>"<p>Detailed 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"=>1354194, "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.g007", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_Payoff_Differences_/1354194", "title"=>"Scatter-plot of Payoff Differences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-25 02:55:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1986560"], "description"=>"<p>Payoff functions for the publisher and adversary for a fixed data sharing strategy <i>g</i>.</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"=>1354207, "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.t003", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Payoff_functions_for_the_publisher_and_adversary_for_a_fixed_data_sharing_strategy_g_/1354207", "title"=>"Payoff functions for the publisher and adversary for a fixed data sharing strategy <i>g</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-25 02:55:57"}

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  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"9", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}

Relative Metric

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