2 Background
We start by providing a short list of background references that frame a particular set of research topics. Abowd & Schmutte (2019) show how to combine formal privacy models with the classic theory of public goods to understand and guide decisions about privacy protection and data dissemination. For the neophyte, Wood et al. (2018) provide a non-technical introduction to formal privacy models in general, and differential privacy in particular. Heffetz & Ligett (2014) also introduce differential privacy, but targeted toward economists. After reading these introductory treatments, you should review the textbook treatment of differential privacy in Dwork & Roth (2014) , focusing on Chapters 1–3. We also recommend consultation of the very fine tutorial “Differential Privacy in the Wild: A tutorial on current practices and open challenges” by Michael Hay, Xi He, and Ashwin Machanavajjhala. The tutorial is available in two parts.
To provide a concrete grounding in practical issues of privacy, Jones (2017) summarizes the history of privacy breaches and privacy protection in the U.S. statistical system. It is important to ask how formal privacy models do, and do not, capture common language and legal interpretations of privacy. As such, we also recommend a review of some of the laws governing data privacy in the U.S. , e.g. 13 U.S. Code (1954) and H.R.4174 (2018) (Confidential Information Protection and Statistical Efficiency Act, also known as CIPSEA). A quick review of the Harvard Privacy Tools website (Harvard University Privacy Tools Project, 2019) can provide a sense of how differential privacy is being implemented in various settings. Goroff (2015) also provides a very useful overview of the key issues in this reading course for a lay audience. Abowd (2017) and Abowd (2016) survey formal privacy systems being implemented at the Census Bureau.
References
13 U.S. Code. (1954). USC: Title 13 - Census Act. Retrieved from https://www.law.cornell.edu/uscode/pdf/lii_usc_TI_13.pdf
Abowd, J. M. (2016). Why statistical agencies need to take privacy-loss budgets seriously, and what it means when they do. The 13th Biennial Federal Committee on Statistical Methodology (FCSM) Policy Conference. Retrieved from https://digitalcommons.ilr.cornell.edu/ldi/32/
Abowd, J. M. (2017). How will statistical agencies operate when all data are private? Journal of Privacy and Confidentiality, 7(3). https://doi.org/10.29012/jpc.v7i3.404
Abowd, J. M., & Schmutte, I. M. (2019). An economic analysis of privacy protection and statistical accuracy as social choices. American Economic Review, 109(1), 171–202. https://doi.org/10.1257/aer.20170627
Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211–407. https://doi.org/10.1561/0400000042
Goroff, D. L. (2015). Balancing privacy versus accuracy in research protocols. Science, 347(6221), 479–480. https://doi.org/10.1126/science.aaa3483
Harvard University Privacy Tools Project. (2019). Homepage. Retrieved from https://privacytools.seas.harvard.edu/
Heffetz, O., & Ligett, K. (2014). Privacy and data-based research. Journal of Economic Perspectives, 28(2), 75–98. https://doi.org/10.1257/jep.28.2.75
H.R.4174. (2018). Confidential Information Protection and Statistical Efficency Act. Retrieved from https://www.congress.gov/bill/115th-congress/house-bill/4174
Jones, C. (2017). Nonconfidential memorandum on Census Bureau privacy breaches. Retrieved from http://doi.org/10.5281/zenodo.1345775
Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., … Vadhan, S. (2018). Differential Privacy: A Primer for a Non-Technical Audience. Vanderbilt Journal of Entertainment and Technology Law, 21(1). Retrieved from http://www.jetlaw.org/journal-archives/volume-21/volume-21-issue-1/differential-privacy-a-primer-for-a-non-technical-audience/