[
[1] Abowd, J. M. (2018). The US Census Bureau adopts differential privacy. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2867–2867).
]Search in Google Scholar
[
[2] Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., . . . others (2020). Google COVID-19 Community Mobility Reports: anonymization process description (version 1.1). arXiv preprint arXiv:2004.04145.
]Search in Google Scholar
[
[3] Almasi, M. M., Siddiqui, T. R., Mohammed, N., & Hemmati, H. (2016). The risk-utility tradeoff for data privacy models. In 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1–5).10.1109/NTMS.2016.7792481
]Search in Google Scholar
[
[4] Assistive AI Makes Replying Easier. (2020). Retrieved from https://www.microsoft.com/en-us/research/group/msai/articles/assistive-ai-makes-replying-easier-2/
]Search in Google Scholar
[
[5] Bavadekar, S., Boulanger, A., Davis, J., Desfontaines, D., Gabrilovich, E., Gadepalli, K., . . . others (2021). Google COVID-19 Vaccination Search Insights: Anonymization Process Description. arXiv preprint arXiv:2107.01179.
]Search in Google Scholar
[
[6] Bavadekar, S., Dai, A., Davis, J., Desfontaines, D., Eckstein, I., Everett, K., . . . others (2020). Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0). arXiv preprint arXiv:2009.01265.
]Search in Google Scholar
[
[7] Biswas, S., Dong, Y., Kamath, G., & Ullman, J. (2020). Coinpress: Practical private mean and covariance estimation. arXiv preprint arXiv:2006.06618.
]Search in Google Scholar
[
[8] Bittner, D. M., Brito, A. E., Ghassemi, M., Rane, S., Sarwate, A. D., & Wright, R. N. (2020). Understanding Privacy-Utility Tradeoffs in Differentially Private Online Active Learning. Journal of Privacy and Confidentiality, 10(2).10.29012/jpc.720
]Search in Google Scholar
[
[9] Bostock, M. (2012). D3.js - Data-Driven Documents. Retrieved from http://d3js.org/
]Search in Google Scholar
[
[10] Brawner, T., & Honaker, J. (2018). Bootstrap inference and differential privacy: Standard errors for free. Unpublished Manuscript.
]Search in Google Scholar
[
[11] Chance, B., Garfield, J., & delMas, R. (2000). Developing Simulation Activities To Improve Students’ Statistical Reasoning.
]Search in Google Scholar
[
[12] chroma.js. (n.d.). Retrieved from https://gka.github.io/chroma.js/
]Search in Google Scholar
[
[13] Cumming, G., & Thomason, N. (1998). Statplay: Multimedia for statistical understanding, in Pereira-Mendoza (ed. In Proceedings of the Fifth International Conference on Teaching Statistics, ISI.
]Search in Google Scholar
[
[14] Cummings, R., Kaptchuk, G., & Redmiles, E. M. (2021). “I need a better description”: An Investigation Into User Expectations For Differential Privacy. ACM CCS.10.1145/3460120.3485252
]Search in Google Scholar
[
[15] delMas, R. C., Garfield, J., & Chance, B. (1999). A model of classroom research in action: Developing simulation activities to improve students’ statistical reasoning. Journal of Statistics Education, 7(3).
]Search in Google Scholar
[
[16] Desfontaines, D. (2020). Lowering the cost of anonymization (Unpublished doctoral dissertation). ETH Zurich.
]Search in Google Scholar
[
[17] Du, W., Foot, C., Moniot, M., Bray, A., & Groce, A. (2020). Differentially private confidence intervals. arXiv preprint arXiv:2001.02285.
]Search in Google Scholar
[
[18] Dwork, C., Kohli, N., & Mulligan, D. (2019). Differential Privacy in Practice: Expose Your Epsilons! Journal of Privacy and Confidentiality, 9(2).10.29012/jpc.689
]Search in Google Scholar
[
[19] Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference (pp. 265–284).10.1007/11681878_14
]Search in Google Scholar
[
[20] Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci..
]Search in Google Scholar
[
[21] Enabling developers and organizations to use differential privacy. (2019). Retrieved from https://developers.googleblog.com/2019/09/enabling-developers-and-organizations.html
]Search in Google Scholar
[
[22] Evans, G., King, G., Schwenzfeier, M., & Thakurta, A. (2020). Statistically valid inferences from privacy protected data. URL: GaryKing.org/dp.
]Search in Google Scholar
[
[23] Fernandes, M., Walls, L., Munson, S., Hullman, J., & Kay, M. (2018). Uncertainty displays using quantile dotplots or cdfs improve transit decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–12).10.1145/3173574.3173718
]Search in Google Scholar
[
[24] Ferrando, C., Wang, S., & Sheldon, D. (2020). General-Purpose Differentially-Private Confidence Intervals. arXiv preprint arXiv:2006.07749.
]Search in Google Scholar
[
[25] Gaboardi, M., Hay, M., & Vadhan, S. (2020). A programming framework for opendp. Manuscript, May.
]Search in Google Scholar
[
[26] Gaboardi, M., Honaker, J., King, G., Murtagh, J., Nissim, K., Ullman, J., & Vadhan, S. (2018). PSI (Ψ): a Private data Sharing Interface.
]Search in Google Scholar
[
[27] Ganta, S. R., Kasiviswanathan, S. P., & Smith, A. (2008). Composition attacks and auxiliary information in data privacy. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 265–273).10.1145/1401890.1401926
]Search in Google Scholar
[
[28] Ge, C., He, X., Ilyas, I. F., & Machanavajjhala, A. (2019). Apex: Accuracy-aware differentially private data exploration. In Proceedings of the 2019 International Conference on Management of Data (pp. 177–194).10.1145/3299869.3300092
]Search in Google Scholar
[
[29] Gigerenzer, G., & Hoffrage, U. (1995). How to improve bayesian reasoning without instruction: frequency formats. Psychological Review, 102(4), 684.10.1037/0033-295X.102.4.684
]Search in Google Scholar
[
[30] Greig, D. M., Porteous, B. T., & Seheult, A. H. (1989). Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society: Series B (Methodological), 51(2), 271–279.
]Search in Google Scholar
[
[31] Haeberlen, A., Pierce, B. C., & Narayan, A. (2011). Differential Privacy Under Fire. In USENIX Security Symposium (Vol. 33).
]Search in Google Scholar
[
[32] Hawes, M. (2020). Differential Privacy and the 2020 Decennial Census. Webinar.
]Search in Google Scholar
[
[33] Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y., Zhang, D., & Bissias, G. (2016). Exploring privacy-accuracy trade-offs using dpcomp. In Proceedings of the 2016 International Conference on Management of Data (pp. 2101–2104).10.1145/2882903.2899387
]Search in Google Scholar
[
[34] Herdağdelen, A., Dow, A., State, B., Mohassel, P., & Pompe, A. (2020). Protecting privacy in Facebook mobility data during the COVID-19 response. Retrieved from https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/
]Search in Google Scholar
[
[35] Hofman, J. M., Goldstein, D. G., & Hullman, J. (2020). How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–12).10.1145/3313831.3376454
]Search in Google Scholar
[
[36] Holohan, N., Braghin, S., Mac Aonghusa, P., & Levacher, K. (2019). Diffprivlib: the IBM differential privacy library. arXiv preprint arXiv:1907.02444.
]Search in Google Scholar
[
[37] Hsu, J., Gaboardi, M., Haeberlen, A., Khanna, S., Narayan, A., Pierce, B. C., & Roth, A. (2014). Differential privacy: An economic method for choosing epsilon. In 2014 IEEE 27th Computer Security Foundations Symposium (pp. 398–410).10.1109/CSF.2014.35
]Search in Google Scholar
[
[38] Hullman, J., Qiao, X., Correll, M., Kale, A., & Kay, M. (2018). In pursuit of error: A survey of uncertainty visualization evaluation. IEEE Transactions on Visualization and Computer Graphics, 25(1), 903–913.10.1109/TVCG.2018.286488930207956
]Search in Google Scholar
[
[39] Hullman, J., Resnick, P., & Adar, E. (2015). Hypothetical outcome plots outperform error bars and violin plots for inferences about reliability of variable ordering. PloS One, 10(11), e0142444.10.1371/journal.pone.0142444464669826571487
]Search in Google Scholar
[
[40] Jarvenpaa, S. L. (1990). Graphic displays in decision making—the visual salience effect. Journal of Behavioral Decision Making, 3(4), 247–262.10.1002/bdm.3960030403
]Search in Google Scholar
[
[41] Kale, A., Kay, M., & Hullman, J. (2020). Visual reasoning strategies for effect size judgments and decisions. IEEE Transactions on Visualization and Computer Graphics.
]Search in Google Scholar
[
[42] Kale, A., Nguyen, F., Kay, M., & Hullman, J. (2018). Hypothetical outcome plots help untrained observers judge trends in ambiguous data. IEEE Transactions on Visualization and Computer Graphics, 25(1), 892–902.10.1109/TVCG.2018.286490930136961
]Search in Google Scholar
[
[43] Karwa, V., & Vadhan, S. (2017). Finite sample differentially private confidence intervals. arXiv preprint arXiv:1711.03908.
]Search in Google Scholar
[
[44] Kasiviswanathan, S. P., & Smith, A. (2014). On the ’semantics’ of differential privacy: A bayesian formulation. Journal of Privacy and Confidentiality, 6(1).10.29012/jpc.v6i1.634
]Search in Google Scholar
[
[45] Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016). When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5092–5103).
]Search in Google Scholar
[
[46] Kho, A. N., Hynes, D. M. D., Goel, S., Solomonides, A. E., Price, R., Hota, B., . . . Others (2014). CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network. Journal of the American Medical Informatics Association, 21(4), 607–611. Retrieved from http://jamia.oxfordjournals.org/content/21/4/607.short10.1136/amiajnl-2014-002827407829824821736
]Search in Google Scholar
[
[47] Kifer, D., & Machanavajjhala, A. (2011). No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (pp. 193–204).10.1145/1989323.1989345
]Search in Google Scholar
[
[48] Kifer, D., & Machanavajjhala, A. (2012). A rigorous and customizable framework for privacy. In Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems (pp. 77–88).10.1145/2213556.2213571
]Search in Google Scholar
[
[49] Lee, J., & Clifton, C. (2011). How Much is Enough? Choosing ɛ for Differential Privacy. In International Conference on Information Security (pp. 325–340).10.1007/978-3-642-24861-0_22
]Search in Google Scholar
[
[50] Li, C., Miklau, G., Hay, M., McGregor, A., & Rastogi, V. (2015). The matrix mechanism: optimizing linear counting queries under differential privacy. The VLDB journal, 24(6), 757–781.10.1007/s00778-015-0398-x
]Search in Google Scholar
[
[51] Liu, C., He, X., Chanyaswad, T., Wang, S., & Mittal, P. (2019). Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing. Proc. Priv. Enhancing Technol., 2019(3), 233–254.10.2478/popets-2019-0045
]Search in Google Scholar
[
[52] Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L. (2008). Privacy: Theory meets practice on the map. In 2008 IEEE 24th International Conference on Data Engineering (pp. 277–286).10.1109/ICDE.2008.4497436
]Search in Google Scholar
[
[53] McKenna, R., Miklau, G., Hay, M., & Machanavajjhala, A. (2018). Optimizing error of high-dimensional statistical queries under differential privacy. Proceedings of the VLDB Endowment, 11(10), 1206–1219.10.14778/3231751.3231769
]Search in Google Scholar
[
[54] McSherry, F. D. (2009). Privacy Integrated Queries: An Extensible Platform for Privacy-preserving Data Analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, series = SIGMOD ’09 (pp. 19–30). New York, NY, USA: ACM. Retrieved from http://doi.acm.org/10.1145/1559845.1559850 doi: 10.1145/1559845.155985010.1145/1559845.1559850
]Search in Google Scholar
[
[55] Messing, S., DeGregorio, C., Hillenbrand, B., King, G., Mahanti, S., Mukerjee, Z., . . . Wilkins, A. (2020). Urls-v3.pdf. In Facebook Privacy-Protected Full URLs Data Set. Harvard Dataverse. Retrieved from https://doi.org/10.7910/DVN/TDOAPG/DGSAMS doi: 10.7910/DVN/TDOAPG/DGSAMS
]Search in Google Scholar
[
[56] Mironov, I. (2012). On significance of the least significant bits for differential privacy. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (pp. 650–661).10.1145/2382196.2382264
]Search in Google Scholar
[
[57] Mironov, I. (2017). Rényi differential privacy. In 2017 IEEE 30th Computer Security Foundations Symposium (CSF) (pp. 263–275).10.1109/CSF.2017.11
]Search in Google Scholar
[
[58] Morgenstern, O., & Von Neumann, J. (1953). Theory of games and economic behavior. Princeton University Press.
]Search in Google Scholar
[
[59] Neyman, J., & Pearson, E. S. (2020). On the use and interpretation of certain test criteria for purposes of statistical inference. Part I. University of California Press.
]Search in Google Scholar
[
[60] Nissim, K., Raskhodnikova, S., & Smith, A. (2007). Smooth sensitivity and sampling in private data analysis. In Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing (pp. 75–84).10.1145/1250790.1250803
]Search in Google Scholar
[
[61] Rivasplata, O. (2012). Subgaussian random variables: An expository note. Internet publication, PDF.
]Search in Google Scholar
[
[62] Rogers, R., Cardoso, A. R., Mancuhan, K., Kaura, A., Gahlawat, N., Jain, N., . . . Ahammad, P. (2020). A Members First Approach to Enabling LinkedIn’s Labor Market Insights at Scale. arXiv preprint arXiv:2010.13981.
]Search in Google Scholar
[
[63] Savage, L. J. (1954). The foundations of statistics. Wiley.
]Search in Google Scholar
[
[64] Schwarz, C. J., & Sutherland, J. (1997). An on-line workshop using a simple capture-recapture experiment to illustrate the concepts of a sampling distribution. Journal of Statistics Education, 5(1).10.1080/10691898.1997.11910523
]Search in Google Scholar
[
[65] Shepp, L. A., & Vardi, Y. (1982). Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging, 1(2), 113–122.10.1109/TMI.1982.430755818238264
]Search in Google Scholar
[
[66] St. John, M. F., Denker, G., Laud, P., Martiny, K., & Pankova, A. (2021). Decision Support for Sharing Data Using Differential Privacy. IEEE Transactions on Visualization and Computer Graphics, 26–35.
]Search in Google Scholar
[
[67] Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557–570.10.1142/S0218488502001648
]Search in Google Scholar
[
[68] Tableau Software. (n.d.). Color Palettes with RGB Values.
]Search in Google Scholar
[
[69] Tang, J., Korolova, A., Bai, X., Wang, X., & Wang, X. (2017). Privacy loss in apple’s implementation of differential privacy on macos 10.12. arXiv preprint arXiv:1709.02753.
]Search in Google Scholar
[
[70] Thaker, P., Budiu, M., Gopalan, P., Wieder, U., & Zaharia, M. (2020). Overlook: Differentially Private Exploratory Visualization for Big Data. arXiv preprint arXiv:2006.12018.
]Search in Google Scholar
[
[71] Wasserman, L., & Zhou, S. (2010). A statistical framework for differential privacy. Journal of the American Statistical Association, 105(489), 375–389.10.1198/jasa.2009.tm08651
]Search in Google Scholar
[
[72] Wilkinson, L. (1999). Dot plots. The American Statistician, 53(3), 276–281.
]Search in Google Scholar
[
[73] Wong, R. C.-W., Fu, A. W.-C., Wang, K., & Pei, J. (2007). Minimality attack in privacy preserving data publishing. In Proceedings of the 33rd International Conference on Very Large Data Bases (pp. 543–554).
]Search in Google Scholar
[
[74] Wright, P. C., & Monk, A. F. (1991). The use of think-aloud evaluation methods in design. ACM SIGCHI Bulletin, 23(1), 55–57.10.1145/122672.122685
]Search in Google Scholar
[
[75] Xiong, A., Wang, T., Li, N., & Jha, S. (2020). Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension. In 2020 IEEE Symposium on Security and Privacy (SP) (pp. 392–410).10.1109/SP40000.2020.00088
]Search in Google Scholar
[
[76] Yang, B., Sato, I., & Nakagawa, H. (2015). Bayesian differential privacy on correlated data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 747–762).10.1145/2723372.2747643
]Search in Google Scholar