Abstract
We describe customized synthetic datasets for publishing mobility data. Companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. However, these companies are disincentivized from sharing data not only to protect the privacy of individuals (drivers and/or passengers), but also to protect their own competitive advantage. Moreover, demographic biases arising from how the services are delivered may be amplified if released data is used in other contexts.
We describe a model and algorithm for releasing origin-destination histograms that removes selected biases in the data using causality-based methods. We compute the origin-destination histogram of the original dataset then adjust the counts to remove undesirable causal relationships that can lead to discrimination or violate contractual obligations with data owners. We evaluate the utility of the algorithm on real data from a dockless bike share program in Seattle and taxi data in New York, and show that these adjusted transportation datasets can retain utility while removing bias in the underlying data.
J. Stoyanovich—This work was supported in part by NSF Grant No. 1741047.
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Notes
- 1.
Many methods for comparing ranked lists have been proposed. We opt for a measure in which identity of the items being ranked (histogram buckets) is deemed important. This is in contrast to typical IR measures such as NDCG or MAP, where item identity is disregarded, and only item quality or relevance scores are retained.
- 2.
Two datasets are neighbors if they differ in the presence or absence of a single record, following the differential privacy definition.
References
Amazon doesn’t consider the race of its customers. should it? Bloomberg (2016)
Acs, G., Castelluccia, C., Chen, R.: Differentially private histogram publishing through lossy compression. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 1–10. IEEE (2012)
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: risk assessments in criminal sentencing. ProPublica, 23 May 2016
Barocas, S., Selbst, A.: Big data’s disparate impact. Calif. Law Rev. 104, 671 (2016)
Brauneis, R., Goodman, E.P.: Algorithmic transparency for the smart city. Yale J. Law Technol., forthcoming
Brock, A.M., et al.: SIG: making maps accessible and putting accessibility in maps. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, p. SIG03. ACM (2018)
Chen, R., Fung, B.C., Yu, P.S., Desai, B.C.: Correlated network data publication via differential privacy. VLDB J. 23(4), 653–676 (2014)
Cormode, G., Procopiuc, M., Srivastava, D., Tran, T.T.: Differentially private publication of sparse data. arXiv preprint arXiv:1103.0825 (2011)
Datta, A., Sen, S., Zick, Y.: Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In: IEEE SP, pp. 598–617 (2016)
Datta, A., Tschantz, M.C., Datta, A.: Automated experiments on ad privacy settings. PoPETs 2015(1), 92–112 (2015)
Day, W.-Y., Li, N.: Differentially private publishing of high-dimensional data using sensitivity control. In Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, pp. 451–462. ACM (2015)
de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1376 (2013)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 259–268. ACM, New York (2015)
Ferris, B., Watkins, K., Borning, A.: OneBusAway: results from providing real-time arrival information for public transit. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1807–1816. ACM (2010)
Galhotra, S., Brun, Y., Meliou, A.: Fairness testing: testing software for discrimination. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, Paderborn, Germany, 4–8 September 2017, pp. 498–510 (2017)
Ge, Y., Knittel, C.R., MacKenzie, D., Zoepf, S.: Racial and gender discrimination in transportation network companies. Working Paper 22776, National Bureau of Economic Research, October 2016
Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. SIAM J. Comput. 41(6), 1673–1693 (2012)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3(1–2), 1021–1032 (2010)
Kilbertus, N., Carulla, M.R., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B.: Avoiding discrimination through causal reasoning. In: Advances in Neural Information Processing Systems, pp. 656–666 (2017)
Kirkpatrick, K.: It’s not the algorithm, it’s the data. Commun. ACM 60(2), 21–23 (2017)
Kumar, R., Vassilvitskii, S.: Generalized distances between rankings. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26–30 April 2010, pp. 571–580 (2010)
Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. In: Advances in Neural Information Processing Systems, pp. 4069–4079 (2017)
Lu, W., Miklau, G., Gupta, V.: Generating private synthetic databases for untrusted system evaluation. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 652–663. IEEE (2014)
Ma, S., Zheng, Y., Wolfson, O.: Real-time city-scale taxi ridesharing. IEEE Trans. Knowl. Data Eng. 27, 1782–1795 (2015)
Markovsky, I.: Low Rank Approximation: Algorithms, Implementation, Applications. Springer, Heidelberg (2011). https://doi.org/10.1007/978-1-4471-2227-2
McFarland, D.A., McFarland, H.R.: Big data and the danger of being precisely inaccurate. Big Data Soc. 2(2), 2053951715602495 (2015)
Meng, X., Li, H., Cui, J.: Different strategies for differentially private histogram publication. J. Commun. Inf. Netw. 2(3), 68–77 (2017)
MetroLab Network. First, do no harm: Ethical guidelines for applying predictive tools within human services (2018, forthcoming). http://www.alleghenycountyanalytics.us/
Nabi, R., Shpitser, I.: Fair inference on outcomes. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, vol. 2018, p. 1931. NIH Public Access (2018)
NYC Taxi and Limousine Commission. TLC trip record data (2018). http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed 2 June 2018
Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 735–746. ACM (2010)
Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stat. Assoc. 100(469), 322–331 (2005)
Salimi, B., Gehrke, J., Suciu, D.: Bias in OLAP queries: detection, explanation, and removal. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1021–1035. ACM (2018)
Sweeney, L.: Discrimination in online Ad delivery. Commun. ACM 56(5), 44–54 (2013)
Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23(8), 1200–1214 (2011)
Xiao, Y., Xiong, L., Fan, L., Goryczka, S.: Dpcube: differentially private histogram release through multidimensional partitioning. arXiv preprint arXiv:1202.5358 (2012)
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., Winslett, M.: Differentially private histogram publication. VLDB J. 22(6), 797–822 (2013)
Zemel, R.S., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: ICML, pp. 325–333 (2013)
Zhang, Y., Thomas, T., Brussel, M., van Maarseveen, M.: Expanding bicycle-sharing systems: lessons learnt from an analysis of usage. PLoS One 11(12), e0168604 (2016)
Zliobaite, I.: Measuring discrimination in algorithmic decision making. Data Min. Knowl. Discov. 31(4), 1060–1089 (2017)
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Rodriguez, L., Salimi, B., Ping, H., Stoyanovich, J., Howe, B. (2019). MobilityMirror: Bias-Adjusted Transportation Datasets. In: Oliveira, J., Farias, C., Pacitti, E., Fortino, G. (eds) Big Social Data and Urban Computing. BiDU 2018. Communications in Computer and Information Science, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-11238-7_2
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