{"id":2546,"date":"2019-04-25T11:23:22","date_gmt":"2019-04-25T15:23:22","guid":{"rendered":"http:\/\/arlduc.org\/senseandscale\/?p=2546"},"modified":"2019-05-02T15:28:44","modified_gmt":"2019-05-02T19:28:44","slug":"civic-hall-ethical-data-collection-spring-2019","status":"publish","type":"post","link":"https:\/\/arlduc.org\/senseandscale\/?p=2546","title":{"rendered":"Civic Hall Ethical Data Collection, Spring 2019"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Participating Organizations<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a rel=\"noreferrer noopener\" aria-label=\"NYC City Council (opens in a new tab)\" href=\"https:\/\/council.nyc.gov\" target=\"_blank\">NYC City Council<\/a><\/li><li><a rel=\"noreferrer noopener\" aria-label=\"Center for Employment Opportunities (opens in a new tab)\" href=\"https:\/\/ceoworks.org\/\" target=\"_blank\">Center for Employment Opportunities<\/a><\/li><li>NCTC<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Collaborative Docs<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a rel=\"noreferrer noopener\" aria-label=\"Data Policies: Good Bits (opens in a new tab)\" href=\"https:\/\/docs.google.com\/document\/d\/1bmYmY-xObs9rLf3vzF54kV2xsG3J3T98Z3ALnJFWC9k\/edit#heading=h.8ala3cvnohki\" target=\"_blank\">Data Policies: Good Bits<\/a><\/li><li><a rel=\"noreferrer noopener\" aria-label=\"Data Policies: Not-So-Good Bits (opens in a new tab)\" href=\"https:\/\/docs.google.com\/document\/d\/1_Nn0o3Ly7MAnvINE5VhytUXb7_bEUMAB02Y3NbwB5i4\/edit#\" target=\"_blank\">Data Policies: Not-So-Good Bits<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Further Resources<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a rel=\"noreferrer noopener\" aria-label=\"Sample dataset (opens in a new tab)\" href=\"https:\/\/drive.google.com\/file\/d\/1KDzdtWopJ-MomczA80s0AtW3WRwgUjyV\/view?usp=sharing\" target=\"_blank\">Sample dataset<\/a><\/li><li><a rel=\"noreferrer noopener\" aria-label=\"adfasd (opens in a new tab)\" href=\"https:\/\/docs.google.com\/document\/d\/1tsuT1h9NCnLQLKN-wn_RKB97vWxmVUbvq9b5SpoJkPI\/edit#\" target=\"_blank\">Further Resources <\/a><\/li><li><a rel=\"noreferrer noopener\" aria-label=\"HIPAA De-Identification Standards (opens in a new tab)\" href=\"https:\/\/www.hhs.gov\/hipaa\/for-professionals\/privacy\/special-topics\/de-identification\/index.html#standard\" target=\"_blank\">HIPAA De-Identification Standards<\/a><\/li><li><strong>Differential Privacy<\/strong><ul><li><a rel=\"noreferrer noopener\" aria-label=\"Harvard Differential Privacy Project (opens in a new tab)\" href=\"https:\/\/privacytools.seas.harvard.edu\/differential-privacy\" target=\"_blank\">Harvard Differential Privacy Project<\/a><\/li><li>Dworkin Video: <a rel=\"noreferrer noopener\" href=\"https:\/\/www.youtube.com\/watch?v=lg-VhHlztqo\" target=\"_blank\">Differential Privacy Video (Mathematical definitions)<\/a><\/li><li>Dworkin Paper: <a rel=\"noreferrer noopener\" href=\"http:\/\/delivery.acm.org\/10.1145\/1870000\/1866758\/p86-dwork.pdf?ip=216.165.95.176&amp;id=1866758&amp;acc=OPEN&amp;key=36E5A5D4E382B3FA%2E36E5A5D4E382B3FA%2E4D4702B0C3E38B35%2E6D218144511F3437&amp;__acm__=1556740119_570ff560a710f66f803b60b162233922\" target=\"_blank\">A Firm Foundation for Private Data Analysis<\/a><\/li><li>Sweeney Paper: <strong><em><a rel=\"noreferrer noopener\" aria-label=\"k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY (opens in a new tab)\" href=\"http:\/\/www.cs.pomona.edu\/~sara\/classes\/cs190-fall12\/k-anonymity.pdf\" target=\"_blank\">k<\/a><\/em><a rel=\"noreferrer noopener\" aria-label=\"k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY (opens in a new tab)\" href=\"http:\/\/www.cs.pomona.edu\/~sara\/classes\/cs190-fall12\/k-anonymity.pdf\" target=\"_blank\">-ANONYMITY: A MODEL FOR PROTECTING PRIVACY<\/a> <\/strong><\/li><li>Dworkin, Roth: <a href=\"http:\/\/www.cis.upenn.edu\/~aaroth\/Papers\/privacybook.pdf\">Differential Privacy Book (PDF)<\/a><\/li><li><strong>Tools<\/strong>:<ul><li>TensorFlow Privacy (<a href=\"https:\/\/medium.com\/tensorflow\/introducing-tensorflow-privacy-learning-with-differential-privacy-for-training-data-b143c5e801b6\">Blog post<\/a> and <a rel=\"noreferrer noopener\" aria-label=\"Github repo (opens in a new tab)\" href=\"https:\/\/github.com\/tensorflow\/privacy\/blob\/master\/tutorials\/walkthrough\/walkthrough.md\" target=\"_blank\">Github repo<\/a>)<\/li><li>Pre-requisite: <a rel=\"noreferrer noopener\" aria-label=\"TensorFlow installation (opens in a new tab)\" href=\"https:\/\/www.tensorflow.org\/install\" target=\"_blank\">TensorFlow installation<\/a> and <a rel=\"noreferrer noopener\" aria-label=\"TensorFlow Neural Network training tutorial (opens in a new tab)\" href=\"https:\/\/www.tensorflow.org\/tutorials\/keras\/basic_classification\" target=\"_blank\">TensorFlow Neural Network training tutorial <\/a><ul><li><a rel=\"noreferrer noopener\" aria-label=\"TensorFlow in notebooks  (opens in a new tab)\" href=\"https:\/\/www.tensorflow.org\/tensorboard\/r2\/tensorboard_in_notebooks\" target=\"_blank\">TensorBoard in notebooks <\/a>could also be useful<\/li><li>This sample <a href=\"https:\/\/colab.research.google.com\/notebooks\/mlcc\/intro_to_neural_nets.ipynb#scrollTo=2I8E2qhyKNd4\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"TensorFlow neural net notebook (opens in a new tab)\">TensorFlow neural net notebook<\/a> could be helpful.<\/li><\/ul><\/li><\/ul><\/li><\/ul><\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Participating Organizations NYC City Council Center for Employment Opportunities NCTC Collaborative Docs Data Policies: Good Bits Data Policies: Not-So-Good Bits Further Resources Sample dataset Further Resources HIPAA De-Identification Standards Differential Privacy Harvard Differential Privacy Project Dworkin Video: Differential Privacy Video (Mathematical definitions) Dworkin Paper: A Firm Foundation for Private Data Analysis Sweeney Paper: k-ANONYMITY: A [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15,14],"tags":[],"class_list":["post-2546","post","type-post","status-publish","format-standard","hentry","category-ethdata","category-more"],"_links":{"self":[{"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/posts\/2546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2546"}],"version-history":[{"count":10,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/posts\/2546\/revisions"}],"predecessor-version":[{"id":2583,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=\/wp\/v2\/posts\/2546\/revisions\/2583"}],"wp:attachment":[{"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2546"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2546"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/arlduc.org\/senseandscale\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}