research-article

Open access

Privacy, anonymity, and big data in the social sciences

Authors:

Jonathan Whittinghill,

Andrew Dean Ho,

Daniel Thomas Seaton,

Isaac ChuangAuthors Info & Claims

Communications of the ACM, Volume 57, Issue 9

Pages 56 - 63

https://doi.org/10.1145/2643132

Published: 01 September 2014 Publication History

All formats PDF

Abstract

Quality social science research and the privacy of human subjects require trust.

References

[1]

Allen, I. E. and Seaman, J. Grade change: Tracking online education in the United States, 2014; http://sloanconsortium.org/publications/survey/grade-change-2013.

Google Scholar

[2]

DeBoer, J., Ho, A.D., Stump, G.S., and Breslow, L. Changing "course:" Reconceptualizing educational variables for Massive Open Online Courses. Educational Researcher. Published online (2013) before print Feb. 7, 2014.

Google Scholar

[3]

Dwork, C. Differential privacy. Automata, languages and programming. Springer Berlin Heidelberg, 2006, 1--12.

Digital Library

Google Scholar

[4]

Dwork, C. State of the Art of Privacy Protection {PowerPoint slides}, 2014; http://web.mit.edu/bigdata-priv/agenda.html.

Google Scholar

[5]

Goben, A. and Salo, D. Federal research data requirements set to change. College & Research Libraries News 74, 8 (2013), 421--425; http://crln.acrl.org/content/74/8/421.full.

Crossref

Google Scholar

[6]

Ho, A.D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J. and Chuang, I. HarvardX and MITx: The First Year of Open Online Courses, Fall 2012--Summer 2013; http://ssrn.com/abstract=2381263

Google Scholar

[7]

Holdren, J.P. Increasing access to the results of federally funded scientific research; http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf.

Google Scholar

[8]

Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B. and Stamper, J. A data repository for the EDM community: The PSLC DataShop. In Handbook of Educational Data Mining. C. Romero, S. Ventura, M. Pechenizkiy, R.SJ.d Baker, eds. CRC Press, Boca Raton, FL. 2010.

Google Scholar

[9]

Machanavajjhala, A., Gehrke, J., Kifer, D. and Venkitasubramaniam, M. l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowledge Discovery from Data 1,1 (2007), 3.

Digital Library

Google Scholar

[10]

MITx and HarvardX. HarvardX-MITx person-course academic year 2013 de-identified dataset, version 2.0. http://dx.doi.org/10.7910/DVN/26147.

Crossref

Google Scholar

[11]

MOOCs @ Illinois. FAQ for Faculty, Feb. 7., 2013; http://mooc.illinois.edu/resources/faqfaculty/

Google Scholar

[12]

National Institutes of Health. Final NIH statement on sharing research data, 2003; http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html.

Google Scholar

[13]

Newman, J. and Oh, S. 8 things you should know about MOOCs. The Chronicle of Higher Education (June 13, 2014); http://chronicle.com/article/8-Things-You-Should-Know-About/146901/.

Google Scholar

[14]

President's Council of Advisors on Science and Technology. Big data and privacy: A technological perspective, 2014; http://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf.

Google Scholar

[15]

Privacy Technical Assistance Center. Frequently asked questions---disclosure avoidance, Oct. 2012; http://ptac.ed.gov/sites/default/files/FAQs_disclosure_avoidance.pdf.

Google Scholar

[16]

Siemens, G. Supporting and promoting learning analytics research. Journal of Learning Analytics 1, 1 (2014), 3--5; http://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/3908/4010.

Crossref

Google Scholar

[17]

Skopek, J.M. Anonymity, the production of goods, and institutional design. Fordham Law Review 82, 4 (2014), 1751--1809; http://ir.lawnet.fordham.edu/flr/vol82/iss4/4/.

Google Scholar

[18]

Sweeney, L. Datafly: A system for providing anonymity in medical data. Database Security, XI: Status and Prospects. T. Lin and S. Qian, eds. Elsevier Science, Amsterdam. 1998.

Digital Library

Google Scholar

[19]

Sweeney, L. Simple demographics often identify people uniquely. Health 671 (2000), 1--34, San Francisco, CA.

Google Scholar

[20]

Sweeney, L. k-anonymity: a model for protecting privacy. Intern. J. on Uncertainty, Fuzziness and Knowledge-based Systems 10, 5 (2002), 557--570.

Digital Library

Google Scholar

[21]

Sweeney, L. Achieving k-anonymity privacy protection using generalization and suppression. Intern. J. on Uncertainty, Fuzziness and Knowledge-based Systems 10, 5, (2002), 571--588.

Digital Library

Google Scholar

[22]

U.S. Department of Education. Family Educational Rights and Privacy (Federal Register Vol. 73, No. 237). U.S. Government Printing Office, Washington, D.C., http://www.gpo.gov/fdsys/pkg/FR-2008-12-09/pdf/E8-28864.pdf

Google Scholar

Cited By

View all

Kolenbrander JHusmann EHenshaw CRheault EBoswell MMichaels A(2024)Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy ExperimentationJournal of Cybersecurity and Privacy10.3390/jcp40300264:3(546-571)Online publication date: 11-Aug-2024
https://doi.org/10.3390/jcp4030026
Enriquez D(2024)Publishing publicly available interview data: an empirical example of the experience of publishing interview dataFrontiers in Sociology10.3389/fsoc.2024.11575149Online publication date: 5-Jun-2024
https://doi.org/10.3389/fsoc.2024.1157514
Wu NYuan XWang SHu HXue MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning ApproachProceedings of the ACM Web Conference 202410.1145/3589334.3645655(3076-3084)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645655
Show More Cited By

Index Terms

Privacy, anonymity, and big data in the social sciences
1. Applied computing
  1. Law, social and behavioral sciences
  2. Life and medical sciences
2. Human-centered computing

Recommendations

Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing

Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While ...
Hiding personalised anonymity of attributes using privacy preserving data mining

Privacy preserving data mining PPDM is a new direction in the area of data mining, where privacy preserving techniques have been applied to maintain the data privacy. Example through the process of data mining the sensitive data of an individual can be ...
Is Privacy possible without Anonymity?: The case for microblogging services
EuroSec '19: Proceedings of the 12th European Workshop on Systems Security

Traditional approaches to privacy are usually based on top of anonymizing or pseudonymizing systems. For example, users who would like to protect their identity and/or hide their activities while browsing the web frequently use anonymizing systems (e.g.,...

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 57, Issue 9

September 2014

94 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2663191

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2014

Published in CACM Volume 57, Issue 9

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Popular
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

76
Total Citations
View Citations
31,019
Total Downloads

Downloads (Last 12 months)4,006
Downloads (Last 6 weeks)54

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kolenbrander JHusmann EHenshaw CRheault EBoswell MMichaels A(2024)Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy ExperimentationJournal of Cybersecurity and Privacy10.3390/jcp40300264:3(546-571)Online publication date: 11-Aug-2024
https://doi.org/10.3390/jcp4030026
Enriquez D(2024)Publishing publicly available interview data: an empirical example of the experience of publishing interview dataFrontiers in Sociology10.3389/fsoc.2024.11575149Online publication date: 5-Jun-2024
https://doi.org/10.3389/fsoc.2024.1157514
Wu NYuan XWang SHu HXue MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning ApproachProceedings of the ACM Web Conference 202410.1145/3589334.3645655(3076-3084)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645655
Zhan CJoksimović SLadjal DRakotoarivelo TMarshall RPardo A(2024)Preserving Both Privacy and Utility in Learning AnalyticsIEEE Transactions on Learning Technologies10.1109/TLT.2024.339376617(1655-1667)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3393766
Ahmad KIqbal WEl-Hassan AQadir JBenhaddou DAyyash MAl-Fuqaha A(2024)Data-Driven Artificial Intelligence in Education: A Comprehensive ReviewIEEE Transactions on Learning Technologies10.1109/TLT.2023.331461017(12-31)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3314610
Aldoseri AAl-Khalifa KHamouda A(2023)Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and ChallengesApplied Sciences10.3390/app1312708213:12(7082)Online publication date: 13-Jun-2023
https://doi.org/10.3390/app13127082
Rao JGao SZhu S(2023)CATS: Conditional Adversarial Trajectory Synthesis for privacy-preserving trajectory data publication using deep learning approachesInternational Journal of Geographical Information Science10.1080/13658816.2023.226255037:12(2538-2574)Online publication date: 9-Oct-2023
https://doi.org/10.1080/13658816.2023.2262550
Harrison AMirsadikov ALuu T(2023)Influence of Media Capabilities on Trust in the Sharing EconomyJournal of Management Information Systems10.1080/07421222.2023.222911840:3(953-982)Online publication date: 23-Aug-2023
https://doi.org/10.1080/07421222.2023.2229118
Buccafurri FDe Angelis VLazzaro S(2023)Enabling anonymized open-data linkage by authorized partiesJournal of Information Security and Applications10.1016/j.jisa.2023.10347874(103478)Online publication date: May-2023
https://doi.org/10.1016/j.jisa.2023.103478
Foster C(2023)Embedding AI in Higher Education: A Call for a Service Design ApproachAI, Blockchain and Self-Sovereign Identity in Higher Education10.1007/978-3-031-33627-0_3(53-65)Online publication date: 23-Jun-2023
https://doi.org/10.1007/978-3-031-33627-0_3
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing

Hiding personalised anonymity of attributes using privacy preserving data mining

Is Privacy possible without Anonymity?: The case for microblogging services

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations