Towards a Modern Approach to Privacy-Aware Government Data
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An Introduction to the Joint Principles for Data Citation
RDAP Review EDITOR’S SUMMARY While the conventions of An Introduction to the Joint Principles for Data Citation bibliographic citation have been by Micah Altman, Christine Borgman, Mercè Crosas and Maryann Martone long established, the sole focus is on reference to other scholarly 3 works. Access to the data serving NOTE: This article summarizes and extends a longer report r of the manuscript must be available to any reader of Science ” e b as the basis for scholarly work has published as [ 1]. Contributors are listed in alphabetical order. We and that “ citations to unpublished data [emphasis added] m u been limited. Data citation extends describe contributions to the paper using a standard taxonomy N and personal communications cannot be used to support , described in [ 2]. Micah Altman and Mercè Crosas were the lead 1 important access to material that 4 authors, taking equal responsibility for revisions and authoring claims in a published paper” [ 4]. Too often, however, this e has been largely unavailable for m the first draft of the manuscript from which this is derived. All u proscription and others like it have been honored only in the l o sharing, verification and reuse. The authors contributed to the conception of the Force 11 principles V breach. Few research articles provide access to the data on – Joint Declaration of Data Citation discussed, to the methodology, to the project administration 5 1 and to the writing through critical review and commentary. which they are based, nor specific citations to data on which 0 Principles, finalized in February 2 the findings rely, nor protocols, algorithms, code or other h 2014, is a formal statement pulling c r a together practices used in the ata citation is rapidly emerging as a key practice technology necessary to reproduce, reuse or extend results. -
Case 1:18-Cv-04727-ELR Document 17-11 Filed 10/19/18 Page 1 of 28
Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 1 of 28 EXHIBIT 10 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 2 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 3 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 4 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 5 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 6 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 7 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 8 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 9 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 10 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 11 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 12 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 13 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 14 of 28 Case 1:18-cv-04727-ELR Document 17-11 Filed 10/19/18 Page 15 of 28 Dr. Michael P. -
00017-89121.Pdf (110.8
March 19, 2014 To: The Federal Trade Commision Re: Mobile Device Tracking From: Micah Altman, Director of Research, MIT Libraries; Non Resident Senior Fellow, Brookings Institution I appreciate the opportunity to contribute to the FTC’s considerations of Mobile Device Tracking. These comments address selected privacy risks and mitigation methods. Our perspective is informed by substantial advances in privacy science that have been made in the computer science literature and by recent research conducted by the members of the Privacy Tools for Sharing Research Data project at Harvard University.1 Scope of information The speakers focussed primarily on businesses use of mobile devices to track consumers’ movements through retail stores and nearby environments. However, as noted in the comments made by the Center for Digital Democracy [CDD 2014] and in the workshop discussion (as documented in the transcript), the the general scope of mobile information tracking businesses and third parties extends far beyond this scenario. Based on the current ability for third parties to collect location information from mobile phones alone, third parties have the potential to collect extensive, fine grained, continuous and identifiable records of a persons location and movement history, accompanied with a partial record of other devices (potentially linked to people) encountered over that history. Information sensitivity Generally, information policy should treat information as sensitive when that information, if linked to a person, even partially or probabilistically, is likely to cause substantial harm. There is a broad range of informational harms that are recognized by regulation and by researchers and 1 The Privacy Tools for Sharing Research Data project is a National Science Foundation funded collaboration at Harvard University involving the Center for Research on Computation and Society, the Institute for Quantitative Social Science, the Berkman Center for Internet & Society, and the Data Privacy Lab. -
Numerical Issues in Statistical Computing for the Social Scientist
Numerical Issues in Statistical Computing for the Social Scientist MICAH ALTMAN JEFF GILL MICHAEL P. McDONALD A JOHN WILEY & SONS, INC., PUBLICATION Numerical Issues in Statistical Computing for the Social Scientist ii WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding, Noel A. C. Cressie, Nicholas I. Fisher, Iain M. Johnstone, J. B. Kadane, Louise M. Ryan, David W. Scott, Adrian F. M. Smith, Jozef L. Teugels; Editors Emeriti: Vic Barnett, J. Stuart Hunter, David G. Kendall A complete list of the titles in this series appears at the end of this volume. Numerical Issues in Statistical Computing for the Social Scientist MICAH ALTMAN JEFF GILL MICHAEL P. McDONALD A JOHN WILEY & SONS, INC., PUBLICATION Copyright c 2004 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, e-mail: [email protected]. -
Numerical Issues in Statistical Computing for the Social Scientist
JSS Journal of Statistical Software April 2005, Volume 12, Book Review 5. http://www.jstatsoft.org/ Reviewer: Frauke Kreuter University of Maryland, College Park Numerical Issues in Statistical Computing for the Social Scientist Micah Altman, Jeff Gill, Michael P. McDonald John Wiley & Sons, Hoboken, NJ, 2004. ISBN 0-471-23633-0. xv + 323 pp. $94.95. http://www.hmdc.harvard.edu/numerical_issues/ This is a very interesting book in an area that hasn’t gotten much attention in the so- cial sciences, but can expect to have more than just a niche audience with the increase of maximum-likelihood-based applications, a heightened interest in simulations, and a general appreciation for computational statistics. Numerical Issues in Statistical Computing for the Social Scientist is the right book for any social scientist who has stumbled upon error mes- sages relating to convergence problems, non-invertible, or ill-conditioned matrices, and who is not just interested in some rough guidance on what to watch out for, but rather wants to understand the source of these problems down to effects of errors in floating point arithmetic. Micah Altman, Jeff Gill, and Michael P. McDonald state in their preface that this book is intended to serve multiple purposes: Introducing new principles, algorithms and solutions while at the same time serving as a guide to statistical computing. There is a benefit to including new research results in a guidebook, but with it comes the challenge to find the right level of difficulty. As a result, the book seems bimodal, sometimes missing the intermediate applied researcher, who writes modest programs within a given statistical package. -
Open Data – an Introduction to the Issue
ITM Web of Conferences 21, 00017 (2018) https://doi.org/10.1051/itmconf/20182100017 CST 2018 Open data – an introduction to the issue Paweł Dymora1,*, and Mirosław Mazurek1, and Bartosz Kowal1 1 Rzeszów University of Technology, Department of Complex Systems, Al. Powstancow Warszawy 12, 35-959 Rzeszów, Poland Abstract. Rapidly developing of internet technologies and digitalization of government generate more and more data. Databases from various public institutions and private sectors, e.g. in the fields of economics, transport, environment and public safety are publishing in the global Internet network, so that any user can browse them without additional charges. Most of this data is published on the open data portals. Open data - that is, "open", public data can allow the processing and analysis of information contained in them completely free of charge. This article is an introduction to a fairly new area of issues such as "open data" or "open government", presents the main mechanisms of accessing to data in public open data portals and also propose a conceptual open data/government model. 1 Open Data The term of “open data” was best defined by the Open Knowledge International non-profit organization [1]. It assumes that open data means the free data that can be freely used, re- used and distributed by anyone for any purpose [2]. This is a somewhat idealized definition of open data. Most published databases do not have all the listed properties [2 - 4]: • Free - access to data must be free, • Availability - data must be available as a whole without any intentional errors. These data must also be available in a convenient and modifiable form, e.g. -
D E Liv E R Ing on T H E D at a R E V Olu T Ion in S U B -S a Ha Ra N a Fr Ic a C
Delivering on the Data Revolution in Sub-Saharan Africa Center for Global Development and the African Population and Health Research Center c Center for Global Development. 2014. Some Rights Reserved. Creative Commons Attribution-NonCommercial 3.0 Center for Global Development 1800 Massachusetts Ave NW, Floor 3 Washington DC 20036 www.cgdev.org CGD is grateful to the Omidyar Network, the UK Department for International Development, and the Hewlett Foundation for support of this work. This research was also made possible through the generous core funding to APHRC by the William and Flora Hewlett Foundation and the Swedish International Development Agency. ISBN 978-1-933286-83-9 Editing, design, and production by Communications Development Incorporated, Washington, D.C. Cover design by Bittersweet Creative. Working Group Working Group Co-chairs Kutoati Adjewoda Koami, African Union Commission Amanda Glassman, Center for Global Development Catherine Kyobutungi, African Population and Health Alex Ezeh, African Population and Health Research Center Research Center Paul Roger Libete, Institut National de la Statistique of Cameroon Working Group Members Themba Munalula, COMESA Angela Arnott, UNECA Salami M.O. Muri, National Bureau of Statistics of Nigeria/ Ibrahima Ba, Institut National de la Statistique, Côte d’Ivoire Samuel Bolaji, National Bureau of Statistics of Nigeria Donatien Beguy, African Population and Health Research Philomena Nyarko, Ghana Statistical Service Center Justin Sandefur, Center for Global Development Misha V. Belkindas, -
Micah Altman
Micah Altman Curriculum Vitae Institute for Quantitative Social Science, Harvard University 1737 Cambridge Street, Cambridge, MA 02138 Phone: (585) 466-4224 Fax: (617) 963-7370 E-mail: [email protected] URL: http://futurelib.org Education Harvard University 1999-2001 Post-doctoral Research Fellow, Department of Government California Institute of Technology 1998 Ph.D., Social Sciences Brown University 1989 B.A., Magna Cum Laude*, Computer Science B.A., Magna Cum Laude*, Ethics and Political Philosophy [*Highest distinction awarded by the University] Honors & Senior Fellow, Information Technology & Politics Section, 2011 Awards American Political Science Association Best Research Software 2009 American Political Science Association (ITP) for BARD Library Technology Excellence Award, Honorable Mention 2009 IGI Global (for The Henry A. Murray Archive) Listed in (Marquis) 2003-4, 2009 Who's Who in America, 57th, 58th, 63rd Edition Best Research Software Award, 2005 American Political Science Association (ITP); (for the VDC System) Annual Meeting Enrichment Fund Award, 2001 Association of American Geographers Best Political Science Research Website, 1999 (for The Record of American Democracy website) Outstanding Dissertation Award, 1999 Western Political Science Association (for the best political science dissertation) Weaver Award, Representation and Electoral Systems Section, 1998 American Political Science Association (for best paper presented at previous meeting) Pre-doctoral Fellowship 1996-7 Harvard-MIT Research Training Group in Political Economy John Randolf Haynes and Dora Haynes Fellowship 1995-6 Anna and James McDonnell Memorial Fellowship 1994-5 Phi Beta Kappa 1989 9/15/2011 Sigma Xi 1989 Research Senior Research Scientist 2006-Present Positions Institute for Quantitative Social Science, Harvard University Archival Director 2007-Present Henry A. -
The Open Data Era in Health and Social Care
THE OPEN DATA ERA IN HEALTH AND SOCIAL CARE A blueprint for the National Health Stefaan Verhulst Beth Simone Noveck Service (NHS England) to develop a Robyn Caplan research and learning programme for the Kristy Brown open data era in health and social care Claudia Paz The Open Data Era in Health and Social Care Table of Contents FOREWORD . 4 EXECUTIVE SUMMARY . 6 INTRODUCTION . 9 PART I: THE OPEN DATA ERA . 11 I.1. Health Data and the Open Data Revolution .......................................................12 I.2. The NHS and Open Data: Where We Stand .......................................................14 PART II: POTENTIAL AND LIMITATIONS OF OPEN DATA . 18 II.1. Value propositions for using Open Health Data ...................................................20 II.1.1. Accountability ............................................................................22 II.1.2. Choice ...................................................................................24 II.1.3. Efficiency .................................................................................27 II.1.4. Outcomes ................................................................................29 II.1.5. Customer Service and Patient satisfaction ...................................................31 II.1.6. Innovation and Economic Growth ...........................................................33 II.2. Potential Challenges and Barriers of Open Data ..................................................36 II.2.1. Cultural and Institutional Barriers ...........................................................36 -
XXIX CICLO UNIVERSITÀ DEGLI STUDI DI CATANIA Dipartimento Di Scienze Politiche
DOTTORATO DI RICERCA IN “SCIENZE POLITICHE” XXIX CICLO UNIVERSITÀ DEGLI STUDI DI CATANIA Dipartimento di Scienze Politiche e Sociali GIUSEPPE REALE Open Government Data. Dall’empowerment del cittadino all’innovazione nella pubblica amministrazione: il caso italiano in un’ottica comparata Coordinatore: prof. F. Sciacca Tutor: prof. F. Mazzeo Rinaldi A.A. 2015/2016 INDICE INTRODUZIONE CAPITOLO 1: “Le trasformazioni in atto nel rapporto tra cittadini e Pubbliche Amministrazioni” 1.1 Verso il “governo aperto”: un’introduzione 1.2 La crisi del paradigma Stato-Nazione tra globalizzazione e nuovo localismo 1.3 Partecipazione e cittadinanza: digitalizzazione, disintermediazione, ibridazione 1.4 Il paradigma dell’Open Government CAPITOLO 2: “Open Data: definizioni, standard e criticità” 2.1 Cosa non è open data 2.2 Open Data: definizioni istituzionali e confini 2.3 Gli standard di pubblicazione e rilascio dei dati aperti 2 2.4 Public data disclosure: la diffusione degli open government data portal tra amministrazione aperta e digital economy. 2.5 Criticità e limiti dei processi di public data disclosure CAPITOLO 3: “Open Governement Data in prospettiva comparata: un percorso di ricerca” 3.1 Il metodo comparativo come strumento di comprensione nelle società globali e interconnesse 3.2 Obiettivi e metodo applicato per l’analisi dell’Open Government Data 3.3 L’analisi del contenuto dei National Action Plan 3.4 L’analisi comparativa dei dati dell’Open Data Barometer 3.5 Le peculiarità del caso italiano 3.6 Modelli di Open Government: una proposta interpretativa CONCLUSIONI BIBLIOGRAFIA 3 Introduzione La ricerca si propone di indagare empiricamente il fenomeno dell’Open Government e dei sistemi Open Data in un’ottica comparativa. -
Open Data, Transparency and Redistricting in Mexico Alejandro Trelles, Micah Altman, Eric Magar and Michael P
Open Data, Transparency and Redistricting in Mexico Alejandro Trelles, Micah Altman, Eric Magar and Michael P. McDonald* Abstract: The many complaints and protests by citizens generated by the deterioration of the political elite in recent decades are clear evidence, among other things, of the urgent need to strengthen the connections between citizens and their representatives. To this end, the delimitation of the electoral boundaries —also known as redistricting— is key to improve political representation. Given the many technicalities involved in this processes —geographic, statistical, digital, among the most obvious— it is easy to succumb to the temptation of relegating it to specialists and lose sight of its importance for democracy. From our perspective, the use of new technologies, as well as the generation and use of open data, offer an opportunity to strengthen political representation. In this article we discuss Mexico’s redistricting experience, the challenges in terms of transparency, and *Alejandro Trelles is a doctoral candidate in Political Science at the University of Pittsburgh, 4600 Wesley W. Posvar Hall, Pittsburgh, PA, 15260. Tel: +1(412) 979 07 15. E-mail: [email protected]. Micah Altman is director of research in the program on Information Science at the Massachusetts Institute of Technology (MIT), E25-131, 77 Massachusetts Ave, Cambridge, Massachusetts, 02139. Tel: +1(585) 466 42 24. E-mail: [email protected]. Eric Magar is a full-time professor of Political Science at the Instituto Tecnológico Autónomo de México (ITAM), Río Hondo 1, Progreso Tiza- pán, México, D.F., 01000. Tel: +52(55) 56 28 40 79. E-mail: [email protected]. -
Practical Approaches to Big Data Privacy Over Time1 Micah Altman,2 Alexandra Wood,3 David R
Practical Approaches to Big Data Privacy Over Time1 Micah Altman,2 Alexandra Wood,3 David R. O’Brien4 & Urs Gasser5 DRAFT (November 6, 2016) Abstract. Increasingly, governments and businesses are collecting, analyzing, and sharing detailed information about individuals over long periods of time. Vast quantities of data from new sources and novel methods for large-scale data analysis promise to yield deeper understanding of human characteristics, behavior, and relationships and advance the state of science, public policy, and innovation. At the same time, the collection and use of fine-grained personal data over time is associated with significant risks to individuals, groups, and society at large. In this article, we examine a range of long- term data collections, conducted by researchers in social science, in order to identify the characteristics of these programs that drive their unique sets of risks and benefits. We also examine the practices that have been established by social scientists to protect the privacy of data subjects in light of the challenges presented in long-term studies. We argue that many uses of big data, across academic, government, and industry settings, have characteristics similar to those of traditional long-term research studies. In this article, we discuss the lessons that can be learned from longstanding data management practices in research and potentially applied in the context of newly emerging data sources and uses. 1. Corporations and governments are collecting data more frequently, and collecting, storing, and using it for longer periods. Commercial and government actors are collecting, storing, analyzing, and sharing increasingly greater quantities of personal information about individuals over progressively long periods of time.