Skip to main content Accessibility help
×
Hostname: page-component-cc8bf7c57-llmch Total loading time: 0 Render date: 2024-12-11T23:09:10.378Z Has data issue: false hasContentIssue false

Chapter 3 - Information Privacy

Challenges and Opportunities for Technology and Measurement

from Part I - Foundations

Published online by Cambridge University Press:  08 November 2023

Louis Tay
Affiliation:
Purdue University, Indiana
Sang Eun Woo
Affiliation:
Purdue University, Indiana
Tara Behrend
Affiliation:
Purdue University, Indiana
Get access

Summary

Organic data have the potential to enable innovative measurements and research designs by virtue of capturing human behavior and interactions in social, educational, and organizational processes. Yet what makes organic data valuable also raises privacy concerns for those individuals whose personal information is being collected and analyzed. This chapter discusses the potential privacy threats posed by organic datasets and the technical tools available to ameliorate such threats. Also noted is the importance for educators and research scientists to participate in interdisciplinary research that addresses the privacy challenges arising from the collection and use of organic data.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abowd, J. M. (2018). The U.S. Census Bureau adopts differential privacy. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2867–2867).Google Scholar
Abril, P. S., Levin, A., & Del Riego, A. (2012). Blurred boundaries: Social media privacy and the twenty-first-century employee. American Business Law Journal, 49(1), 63124.CrossRefGoogle Scholar
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509514.CrossRefGoogle ScholarPubMed
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2020). Secrets and likes: The drive for privacy and the difficulty of achieving it in the digital age. Journal of Consumer Psychology, 30(4), 736758.CrossRefGoogle Scholar
Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision making. IEEE Security & Privacy, 3(1), 2633.CrossRefGoogle Scholar
Adjerid, I., Peer, E., & Acquisti, A. (2018). Beyond the privacy paradox: Objective versus relative risk in privacy decision making. MIS Quarterly, 42(2), 465488.CrossRefGoogle Scholar
Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (pp. 439–450).CrossRefGoogle Scholar
Alge, B. J., Ballinger, G. A., Tangirala, S., & Oakley, J. L. (2006). Information privacy in organizations: Empowering creative and extrarole performance. Journal of Applied Psychology, 91(1), 221232.CrossRefGoogle ScholarPubMed
Alsarkal, Y., Zhang, N., & Xu, H. (2018). Your privacy is your friend’s privacy: Examining interdependent information disclosure on online social networks. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 892–901).CrossRefGoogle Scholar
Altman, I. (1974). Privacy: A conceptual analysis. In Carson, D. H. (Ed.), Man-environment interactions: Evaluations and applications: Part 2 (pp. 1328). Environmental Design Research Association.Google Scholar
Article 29 Data Protection Working Party. (2014). Opinion 05/2014 on anonymisation techniques. https://ec.europa.eu/justice/article-29/documentation/Google Scholar
Auxier, B., Rainie, L., Anderson, M., Perrin, A., Kumar, M., & Turner, E. (2019). Americans and privacy: Concerned, confused and feeling lack of control over their personal information. Pew Research Center. https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/Google Scholar
Barbaro, M., Zeller, T., & Hansell, S. (2006). A face is exposed for AOL searcher no. 4417749. New York Times. https://www.nytimes.com/2006/08/09/technology/09aol.htmlGoogle Scholar
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber?. Psychological Science, 26(10), 15311542.CrossRefGoogle ScholarPubMed
Beresford, A. R., Kübler, D., & Preibusch, S. (2012). Unwillingness to pay for privacy: A field experiment. Economics Letters, 117(1), 2527.CrossRefGoogle Scholar
Bernardi, C., & Maday, Y. (1997). Spectral methods. In Ciarlet, P. G. & Lions, J. L. (Eds.), Handbook of numerical analysis (Vol. 5; pp. 209485). Elsevier.Google Scholar
Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications for subjective survey data. American Economic Review, 91(2), 6772.CrossRefGoogle Scholar
Bhave, D. P., Teo, L. H., & Dalal, R. S. (2020). Privacy at work: A review and a research agenda for a contested terrain. Journal of Management, 46(1), 127164.CrossRefGoogle Scholar
Brandeis, L., & Warren, S. (1890). The right to privacy. Harvard Law Review, 4(5), 193220.Google Scholar
Comarela, G., Durairajan, R., Barford, P., Christenson, D., & Crovella, M. (2018). Assessing candidate preference through web browsing history. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 158–167).CrossRefGoogle Scholar
Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online privacy: Attitudes, behaviors, and unintended consequences. Journal of Computer‐Mediated Communication, 15(1), 83108.CrossRefGoogle Scholar
Dienlin, T., & Trepte, S. (2015). Is the privacy paradox a relic of the past? An in‐depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, 45(3), 285297.CrossRefGoogle Scholar
Dinev, T., McConnell, R. A., & Smith, H. J. (2015). Informing privacy research through information systems, psychology, and behavioral economics: Thinking outside the “APCO” box. Information Systems Research, 26(4), 639655.CrossRefGoogle Scholar
Dong, Y., Yang, Y., Tang, J., Yang, Y., & Chawla, N. V. (2014, August). Inferring user demographics and social strategies in mobile social networks. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 15–24).CrossRefGoogle Scholar
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference (pp. 265284). Springer.CrossRefGoogle Scholar
Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211407.CrossRefGoogle Scholar
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014). Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (pp. 1054–1067).CrossRefGoogle Scholar
Fung, B. C., Wang, K., Chen, R., & Yu, P. S. (2010). Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys, 42(4), 153.CrossRefGoogle Scholar
Ganta, S. R., Kasiviswanathan, S. P., & Smith, A. (2008, August). 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).CrossRefGoogle Scholar
Gates, G. W. (2011). How uncertainty about privacy and confidentiality is hampering efforts to more effectively use administrative records in producing U.S. national statistics. Journal of Privacy and Confidentiality, 3(2), 3–40.CrossRefGoogle Scholar
Groves, R. M. (2011). Three eras of survey research. Public Opinion Quarterly, 75(5), 861871.CrossRefGoogle Scholar
Gymrek, M., McGuire, A. L., Golan, D., Halperin, E., & Erlich, Y. (2013). Identifying personal genomes by surname inference. Science, 339(6117), 321324.CrossRefGoogle ScholarPubMed
Hao, K. (2018, October 21). Establishing an AI code of ethics will be harder than people think. MIT Technology Review.Google Scholar
Harford, T. (2014). Big data: A big mistake?. Significance, 11(5), 1419.CrossRefGoogle Scholar
Hoffmann, C. P., Lutz, C., & Ranzini, G. (2016). Privacy cynicism: A new approach to the privacy paradox. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 10(4).CrossRefGoogle Scholar
Hong, W., & Thong, J. Y. (2013). Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly, 37(1), 275298.CrossRefGoogle Scholar
Hu, J., Zeng, H. J., Li, H., Niu, C., & Chen, Z. (2007). Demographic prediction based on user’s browsing behavior. In Proceedings of the 16th International Conference on World Wide Web (pp. 151–160).CrossRefGoogle Scholar
Huang, Z., Du, W., & Chen, B. (2005). Deriving private information from randomized data. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (pp. 37–48).CrossRefGoogle Scholar
Jia, H., Wisniewski, P. J., Xu, H., Rosson, M. B., & Carroll, J. M. (2015). Risk-taking as a learning process for shaping teen’s online information privacy behaviors. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 583–599).CrossRefGoogle Scholar
John, L. K., Acquisti, A., & Loewenstein, G. (2011). Strangers on a plane: Context-dependent willingness to divulge sensitive information. Journal of Consumer Research, 37(5), 858873.CrossRefGoogle Scholar
Jones, R., Kumar, R., Pang, B., & Tomkins, A. (2007). “I know what you did last summer” query logs and user privacy. In Proceedings of the 16th ACM Conference on Information and Knowledge Management (pp. 909–914).Google Scholar
Knoke, D., & Yang, S. (2019). Social network analysis. Sage Publications.Google Scholar
Koelmeyer, A., & Josey, N. (2019). Employment and privacy: Consent, the ‘privacy act’ and biometric scanners in the workplace. LSJ: Law Society of NSW Journal, 57, 7677.Google Scholar
Krauss, R. M., Freyberg, R., & Morsella, E. (2002). Inferring speakers’ physical attributes from their voices. Journal of Experimental Social Psychology, 38(6), 618625.CrossRefGoogle Scholar
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: Traps in big data analysis. Science, 343(6176), 12031205.CrossRefGoogle ScholarPubMed
Lederer, S., Hong, J. I., Dey, A. K., & Landay, J. A. (2004). Personal privacy through understanding and action: Five pitfalls for designers. Personal and Ubiquitous Computing, 8(6), 440454.CrossRefGoogle Scholar
Lee, D. (2018, November 27). Predictim babysitter app: Facebook and Twitter take action. BBC News. https://bbc.comGoogle Scholar
Lelkes, Y., Krosnick, J. A., Marx, D. M., Judd, C. M., & Park, B. (2012). Complete anonymity compromises the accuracy of self-reports. Journal of Experimental Social Psychology, 48(6), 12911299.CrossRefGoogle Scholar
Li, H., Zhu, H., & Ma, D. (2017). Demographic information inference through meta-data analysis of Wi-Fi traffic. IEEE Transactions on Mobile Computing, 17(5), 10331047.CrossRefGoogle Scholar
Loukides, G., Denny, J. C., & Malin, B. (2010). The disclosure of diagnosis codes can breach research participants’ privacy. Journal of the American Medical Informatics Association, 17(3), 322327.CrossRefGoogle ScholarPubMed
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007). l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 3es.CrossRefGoogle Scholar
Margulis, S. T. (1977). Conceptions of privacy: Current status and next steps. Journal of Social Issues, 33(3), 521.CrossRefGoogle Scholar
Marreiros, H., Tonin, M., Vlassopoulos, M., & Schraefel, M. C. (2017). “Now that you mention it”: A survey experiment on information, inattention and online privacy. Journal of Economic Behavior & Organization, 140, 117.CrossRefGoogle Scholar
McFarland, A. D., Lewis, K., & Goldberg, A. (2016). Sociology in the era of big data: The ascent of forensic social science. American Sociologist, 47, 1235.CrossRefGoogle Scholar
McFarland, D. A., & McFarland, H. R. (2015). Big data and the danger of being precisely inaccurate. Big Data & Society, July–December, 1–4.CrossRefGoogle Scholar
Merton, R. K. (1968). Social theory and social structure. Simon & Schuster.Google Scholar
National Research Council. (2011). The importance of common metrics for advancing social science theory and research: A workshop summary. National Academies Press.Google Scholar
National Science and Technology Council. (2016). National privacy research strategy. https://www.nitrd.gov/pubs/NationalPrivacyResearchStrategy.pdfGoogle Scholar
Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In Proceedings of the IEEE Symposium on Security and Privacy (pp. 111–125). IEEE.CrossRefGoogle Scholar
Neace, G. (2019). Biometric privacy: Blending employment law with the growth of technology. UIC Law Review, 53, 73112.Google Scholar
Nissenbaum, H. (2020). Privacy in context. Stanford University Press.Google Scholar
O’Neill, L., Dexter, F., & Zhang, N. (2016). The risks to patient privacy from publishing data from clinical anesthesia studies. Anesthesia & Analgesia, 122(6), 20172027.CrossRefGoogle ScholarPubMed
Osborne, C. (1991). Statistical calibration: A review. International Statistical Review, 59(3), 309336.CrossRefGoogle Scholar
Oswald, F. L., Behrend, T. S., Putka, D. J., & Sinar, E. (2020). Big data in industrial-organizational psychology and human resource management: Forward progress for organizational research and practice. Annual Review of Organizational Psychology and Organizational Behavior, 7, 505533.CrossRefGoogle Scholar
Paxton, A., & Griffiths, T. L. (2017). Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behavior Research Methods, 49(5), 16301638.CrossRefGoogle ScholarPubMed
Peterson, D. (2016). Edtech and student privacy: California law as a model. Berkeley Technology Law Journal, 31(2), 961995.Google Scholar
Post, R. C. (2017). Data privacy and dignitary privacy: Google Spain, the right to be forgotten, and the construction of the public sphere. Duke Law Journal, 67, 9811072.Google Scholar
Powell, E. E., & Baker, T. (2014). It’s what you make of it: Founder identity and enacting strategic responses to adversity. Academy of Management Journal, 57(5), 14061433.CrossRefGoogle Scholar
Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100126.CrossRefGoogle Scholar
Ravid, D. M., White, J. C., & Behrend, T. S. (2021). Implications of COVID-19 for privacy at work. Industrial and Organizational Psychology, 14(1–2), 194198.CrossRefGoogle Scholar
Reynolds, B., Venkatanathan, J., Gonçalves, J., & Kostakos, V. (2011, September). Sharing ephemeral information in online social networks: Privacy perceptions and behaviours. In IFIP Conference on Human-Computer Interaction (pp. 204215). Springer.Google Scholar
Rocher, L., Hendrickx, J. M., & De Montjoye, Y. A. (2019). Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications, 10(1), 19.CrossRefGoogle ScholarPubMed
Ruggles, S. (2014). Big microdata for population research. Demography, 51(1), 287297.CrossRefGoogle ScholarPubMed
Russom, M. B., Sloan, R. H., & Warner, R. (2011). Legal concepts meet technology: A 50-state survey of privacy laws. In Proceedings of the 2011 Workshop on Governance of Technology, Information, and Policies (pp. 29–37).CrossRefGoogle Scholar
Schoeman, F. D. (Ed.). (1984). Philosophical dimensions of privacy: An anthology. Cambridge University Press.CrossRefGoogle Scholar
Sheehan, K. B., & Hoy, M. G. (1999). Flaming, complaining, abstaining: How online users respond to privacy concerns. Journal of Advertising, 28(3), 3751.CrossRefGoogle Scholar
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 9891015.CrossRefGoogle Scholar
Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167196.CrossRefGoogle Scholar
Solove, D. J. (2006). A taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477560.CrossRefGoogle Scholar
Solove, D. J. (2007). ‘I’ve got nothing to hide’ and other misunderstandings of privacy. San Diego Law Review, 44, 745772.Google Scholar
Solove, D. J. (2021). The myth of the privacy paradox. George Washington Law Review, 89(1), 1–51.Google Scholar
Sweeney, L. (2000). Simple demographics often identify people uniquely. Health (San Francisco), 671(2000), 134.Google Scholar
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557570.CrossRefGoogle Scholar
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.Google Scholar
Thomson, J. J. (1975). The right to privacy. Philosophy & Public Affairs, 4(4), 295314.Google Scholar
Walker, R. K. (2012). The right to be forgotten. Hastings Law Journal, 64, 257286.Google Scholar
Wang, Y. X. (2018). Revisiting differentially private linear regression: Optimal and adaptive prediction & estimation in unbounded domain. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (pp. 93–103).Google Scholar
Wang, Z., Li, S., Shi, H., & Zhou, G. (2014). Skill inference with personal and skill connections. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical papers (pp. 520–529).Google Scholar
Weber, I., & Castillo, C. (2010). The demographics of web search. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 523–530).CrossRefGoogle Scholar
Weinstein, M. A. (1971). The uses of privacy in the good life. In Pennock, J. R. & Chapman, J. W. (Eds.), Nomos XIII: Privacy (pp. 624692). Atherton Press.Google Scholar
Westin, A. F. (1967). Privacy and freedom. Atheneum.Google Scholar
Wittes, B., & Liu, J. C. (2015, May 21). The privacy paradox: The privacy benefits of privacy threats. Center for Technology Innovation at Brookings.Google Scholar
Wood-Doughty, Z., Andrews, N., Marvin, R., & Dredze, M. (2018). Predicting Twitter user demographics from names alone. In Proceedings of the 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (pp. 105–111).CrossRefGoogle Scholar
Xu, H., Teo, H. H., Tan, B. C., & Agarwal, R. (2012). Effects of individual self-protection, industry self-regulation, and government regulation on privacy concerns: A study of location-based services. Information Systems Research, 23(4), 13421363.CrossRefGoogle Scholar
Xu, H., & Zhang, N. (2022). From contextualizing to context-theorizing: Assessing context effects in privacy research. Management Science, 68(10), 70657791.CrossRefGoogle Scholar
Yo, T., & Sasahara, K. (2017). Inference of personal attributes from tweets using machine learning. In 2017 IEEE International Conference on Big Data (pp. 31683174). IEEE.CrossRefGoogle Scholar
Yoo, J. S., Thaler, A., Sweeney, L., & Zang, J. (2018). Risks to patient privacy: A re-identification of patients in Maine and Vermont statewide hospital data. Journal of Technology and Science Education, 2018, 2018100901.Google Scholar
Zhong, Y., Yuan, N. J., Zhong, W., Zhang, F., & Xie, X. (2015). You are where you go: Inferring demographic attributes from location check-ins. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (pp. 295–304).CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×