Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3631985acmotherbooksBook PagePublication Pageseducational-resourcesacm-pubtype
Identification: A Teaching Moment for Privacy and DatabasesJanuary 2023
  • Authors:
  • Matthew P. Dube,
  • Rocko Graziano
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
ISBN:979-8-4007-0470-3
Published:26 January 2024
Pages:
4
Bibliometrics
Skip Abstract Section
Abstract

This learning experience helps students gain experience and proficiency with issues regarding the ethical collection and use of data. Students will gain an appreciation for the risks associated with record-level identification, where data attributes, however innocently collected, can and have been used to violate privacy and lead to discrimination against individuals and protected classes of individuals.

Educational-resources Downloads

ZIP
ecse-2022-0007-File002.zip

This zip contains the supplemental materials for this article.

References

  1. Darrow, J. & Lichtenstein, S. (2008). Do You Really Need My Social Security Number - Data Collection Practices in the Digital Age, 10 N.C. J.L. & Tech. 1. Available at: https://scholarship.law.unc.edu/ncjolt/vol10/iss1/2Google ScholarGoogle Scholar
  2. Hunt, D. B,2005. Redlining, Encyclopedia of Chicago, http://www.encyclopedia.chicagohistory.org/pages/1050.htmlGoogle ScholarGoogle Scholar
  3. Bopp, C., Benjamin, L. M., & Voida, A. (2019). The coerciveness of the primary key: Infrastructure problems in human services work. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1--26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Clark, J., Cormack, L., & Wang, S. (2021). Privacy Concerns in New York City Elections. Technical Report, Princeton University.Google ScholarGoogle Scholar
  5. Young, C., Martin, D., & Skinner, C. (2009). Geographically intelligent disclosure control for flexible aggregation of census data. International Journal of Geographical Information Science, 23(4), 457--482.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Tschantz, M. C., (2022). What is Proxy Discrimination? In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, New York, NY, USA, 1993--2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Egenhofer, M., and Dube, M. 2009. Topological Relations from Metric Refinements. Proceedings of the 17th ACM SIGSPATIAL 2009, 158--167.Google ScholarGoogle Scholar
  8. Dube, M., Barrett, J., and Egenhofer, M. 2015. From Metric to Topology: Determining Relations in Discrete Space. Proceedings of the 2015 International Conference on Spatial Information Theory, 151--171.Google ScholarGoogle Scholar
  9. Colloff, M. F., Wade, K. A., & Strange, D. (2016). Unfair lineups make witnesses more likely to confuse innocent and guilty suspects. Psychological Science, 27(9), 1227--1239.Google ScholarGoogle ScholarCross RefCross Ref
  10. Steblay, N. K., & Wells, G. L. (2020). Assessment of bias in police lineups. Psychology, Public Policy, and Law, 26(4), 393--412. Google ScholarGoogle ScholarCross RefCross Ref
  11. Longfield, J (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal of Teaching and Learning in Higher Education, v21 n2 p266--271.Google ScholarGoogle Scholar
  12. Kapil, G., Agrawal, A., & Khan, R. A. (2016, October). A study of big data characteristics. In 2016 International Conference on Communication and Electronics Systems (ICCES) (pp. 1--4). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  13. Denning, D. E., Akl, S. G., Heckman, M., Lunt, T. F., Morgenstern, M., Neumann, P. G., & Schell, R. R. (1987). Views for multilevel database security. IEEE Transactions on Software Engineering, (2), 129--140.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. De Groot, J. (2022). The History of Data Breaches [Online]. Available: https://www.digitalguardian.com/blog/history-data-breaches.Google ScholarGoogle Scholar
  15. Throne, R. (2022). Adverse Trends in Data Ethics: The AI Bill of Rights and Human Subjects Protections. Available at SSRN: https://ssrn.com/abstract=4279922 or Google ScholarGoogle ScholarCross RefCross Ref
  16. Angwin, Larson, Mattu, Kirchner (2016). Machine Bias, ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencingGoogle ScholarGoogle Scholar
  17. Mangasarian, O., Street W., and Wolberg, W. 1995. Breast cancer diagnosis and prognosis via linear programming. Operations research 43.4, 570--577.Google ScholarGoogle Scholar
Contributors
  • University of Maine
  • University of Maine
Index terms have been assigned to the content through auto-classification.

Recommendations