Learning Personalized Privacy Preference from Public Data
Published Online:13 Jun 2024https://doi.org/10.1287/isre.2023.0318
References
- (2009) Predicting social security numbers from public data. Proc. Natl. Acad. Sci. USA 106(27):10975–10980.Crossref, Google Scholar
- (2022) How privacy’s past may shape its future. Science 375(6578):270–272.Crossref, Google Scholar
- (2015) Privacy and human behavior in the age of information. Science 347(6221):509–514.Crossref, Google Scholar
- (2018) The impact of user personality traits on word of mouth: Text-mining social media platforms. Inform. Systems Res. 29(3):612–640.Link, Google Scholar
- (2019) Choice architecture, framing, and cascaded privacy choices. Management Sci. 65(5):2267–2290.Abstract, Google Scholar
- (2018) Beyond the privacy paradox: Objective vs. relative risk in privacy decision making. MIS Quart. 42(2):465–488.Crossref, Google Scholar
- (2022) Too tired and in too good of a mood to worry about privacy: Explaining the privacy paradox through the lens of effort level in information processing. Inform. Systems Res. 34(4):1415–1436.Link, Google Scholar
- (2019) Predicting users mobile app privacy preferences. J. Comput. Comm. 7(10):147–156.Crossref, Google Scholar
- (2020) Mobile application security: Role of perceived privacy as the predictor of security perceptions. Internat. J. Inform. Management 52:102063.Crossref, Google Scholar
- (2010) The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems 49(2):138–150.Crossref, Google Scholar
- (2019) “What if?” Predicting individual users’ smart home privacy preferences and their changes. Proc. Privacy Enhancing Tech. Sympos. 2019(4):211–231.Crossref, Google Scholar
- (1999) An investigation of gender differences in on-line privacy concerns and resultant behaviors. J. Interactive Marketing 13(4):24–38.Crossref, Google Scholar
- (2017) The privacy paradox–investigating discrepancies between expressed privacy concerns and actual online behavior–A systematic literature review. Telematics Informatics 34(7):1038–1058.Crossref, Google Scholar
- (2002) Trustworthiness in electronic commerce: The role of privacy, security, and site attributes. J. Strategic Inform. Systems 11(3–4):245–270.Crossref, Google Scholar
- (2005) Privacy in e-commerce: Stated preferences vs. actual behavior. Comm. ACM 48(4):101–106.Crossref, Google Scholar
- (2012) Predicting location-sharing privacy preferences in social network applications. Proc. First Workshop Recent Adv. Behav. Prediction Pro-active Pervasive Comput. (AwareCast), 1–12.Google Scholar
- (2008) Psychosocial factors at work and risk of depression: A systematic review of the epidemiological evidence. Occupational Environ. Medicine 65(7):438–445.Crossref, Google Scholar
- (1993) Psychosocial factors at work and musculoskeletal disease. Scandinavian J. Work Environ. Health 19(5):297–312.Crossref, Google Scholar
- (2022) Prediction of mobile app privacy preferences with user profiles via federated learning. Proc. Twelfth ACM Conf. Data Appl. Security Privacy (ACM, New York), 89–100.Google Scholar
- (2004) Protecting personal information online: A survey of user privacy concerns and control techniques. J. Comput. Inform. Systems 44(4):85–92.Google Scholar
- (1995) Consumer awareness of name removal procedures: Implications for direct marketing. J. Direct Marketing 9(2):10–19.Crossref, Google Scholar
- (1999) Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organ. Sci. 10(1):104–115.Link, Google Scholar
- (2015) Predicting privacy behavior on online social networks. Proc. Internat. AAAI Conf. Web Social Media, vol. 9 (AAAI Press, Palo Alto, CA), 91–100.Google Scholar
- (2015) Privacy concerns for use of voice activated personal assistant in the public space. Internat. J. Human Comput. Interaction 31(4):307–335.Crossref, Google Scholar
- (2005) Psychosocial factors and cardiovascular diseases. Annual Rev. Public Health 26:469–500.Crossref, Google Scholar
- (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Sci. 31(3):493–520.Link, Google Scholar
- (1991) Privacy: Recognition of a consumer right. J. Public Policy Marketing 10(1):149–166.Crossref, Google Scholar
- (2022) Emotion English DistilRoBERTa-base. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/.Google Scholar
- (2002) The platform for privacy preference as a social protocol: An examination within the US policy context. ACM Trans. Internet Tech. 2(4):276–306.Crossref, Google Scholar
- (2021) Drivers and inhibitors of Internet privacy concern: A multidimensional development theory perspective. J. Bus. Ethics 168(3):539–564.Crossref, Google Scholar
- (2000) Systematic review of psychosocial factors at work and private life as risk factors for back pain. Spine 25(16):2114–2125.Crossref, Google Scholar
- IBISworld (2020) Educational services in the US market size 2005–2026. Retrieved August 24, https://www.ibisworld.com/industry-statistics/market-size/educational-services-united-states/.Google Scholar
- (2016) Privacy vs. reward: Do loyalty programs increase consumers’ willingness to share personal information with third-party advertisers and data brokers? J. Retailing Consumer Services 28:296–303.Crossref, Google Scholar
- (2013) Research note—Privacy concerns and privacy-protective behavior in synchronous online social interactions. Inform. Systems Res. 24(3):579–595.Link, Google Scholar
- (2017) LightGBM: A highly efficient gradient boosting decision tree. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 3149–3157.Google Scholar
- (1977) The concept privacy and its biological basis. J. Soc. Issues 33(3):52–65.Crossref, Google Scholar
- (2015) Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Quart. 49(4):757–786.Crossref, Google Scholar
- (2008) Selected antecedents of customer loyalty within a restaurant loyalty program: Perceived control, privacy concern, perceived value of a loyalty program, and willingness to disclose information. Unpublished doctoral dissertation, The Pennsylvania State University, State College, University Park, PA.Google Scholar
- (2023) Predicting consumer in-store purchase using real-time retail video analytics. Preprint, submitted July 18, http://dx.doi.org/10.2139/ssrn.4513385.Google Scholar
- (2017) Resolving the privacy paradox: Toward a cognitive appraisal and emotion approach to online privacy behaviors. Inform. Management 54(8):1012–1022.Crossref, Google Scholar
- (2016) A structured analysis of unstructured big data by leveraging cloud computing. Marketing Sci. 35(3):363–388.Link, Google Scholar
- (2004) Inducing customers to disclose personal information to Internet businesses with social adjustment benefits. Proc. Internat. Conf. Inform. Systems (ICIS) (AIS eLibrary, Atlanta).Google Scholar
- (2017) A unified approach to interpreting model predictions. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 4768–4777.Google Scholar
- (2004) Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Inform. Systems Res. 15(4):336–355.Link, Google Scholar
- (2021) The psychosocial development theory of Erik Erikson: Critical overview. Early Child Development Care 191(7–8):1107–1121.Crossref, Google Scholar
- (2017) “Now that you mention it”: A survey experiment on information, inattention and online privacy. J. Econom. Behav. Organ. 140:1–17.Crossref, Google Scholar
- (2022) Enhancing user privacy in mobile devices through prediction of privacy preferences. Comput. Security ESORICS 2022 27th Eur. Sympos. Res. Comput. Security Proc. Part I (Springer, Berlin), 153–172.Google Scholar
- (2007) Communication privacy management in electronic commerce. J. Comput. Mediated Comm. 12(2):335–361.Crossref, Google Scholar
- (2000) Information privacy: Corporate management and national regulation. Organ. Sci. 11(1):35–57.Link, Google Scholar
- (2000) Behavioral economics. NBER Working Paper No. 7948, National Bureau of Economic Research, Cambridge, MA.Google Scholar
- (2015) Analyzing and predicting privacy settings in the social web. User Model. Adaptation Personalization 23rd Internat. Conf. UMAP 2015. Proc. (Springer, Berlin), 104–117.Google Scholar
- (2023) Are you what you tweet? The impact of sentiment on digital news consumption and social media sharing. Inform. Systems Res. 34(1):111–136.Link, Google Scholar
- (2011) State of the information privacy literature: Where are we now and where should we go? MIS Quart. 35(4):977–988.Crossref, Google Scholar
- (2000) Privacy concerns and consumer willingness to provide personal information. J. Public Policy Marketing 19(1):27–41.Crossref, Google Scholar
- (2016) Why we share: A uses and gratifications approach to privacy regulation in social media use. J. Broadcasting Electronic Media 60(1):61–86.Crossref, Google Scholar
- (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proc. 2019 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Kerrville, TX), 3982–3992.Google Scholar
- (2023) Frontiers: Polarized America: From political polarization to preference polarization. Marketing Sci. 42(1):48–60.Link, Google Scholar
- (2023) Predicting privacy preferences for smart devices as norms. Preprint, submitted February 21, https://arxiv.org/abs/2302.10650.Google Scholar
- (2000) Dimensions of privacy concern among online consumers. J. Public Policy Marketing 19(1):62–73.Crossref, Google Scholar
- (2001) Information privacy and marketing: What the US should (and shouldn’t) learn from Europe. Calif. Management Rev. 43(2):8–33.Crossref, Google Scholar
- (2011) Information privacy research: An interdisciplinary review. MIS Quart. 35(4):989–1015.Crossref, Google Scholar
- (2022) Policy impacts of statistical uncertainty and privacy. Science 377(6609):928–931.Crossref, Google Scholar
- (2004) Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Inform. Management 41(6):747–762.Crossref, Google Scholar
- (2020) Point-of-interest type inference from social media text. Preprint, submitted September 30, https://arxiv.org/abs/2009.14734.Google Scholar
- (2023) Divergent thinking and online videos: A study of TED talks via multi-modal video analytics. Preprint, submitted September 8, https://ssrn.com/abstract=4566394.Google Scholar
- (2022) Frontiers: How support for Black Lives Matter impacts consumer responses on social media. Marketing Sci. 41(6):1029–1044.Link, Google Scholar
- (2024) Predicting instructor performance in online education: An interpretable hierarchical transformer with contextual attention. Inform. Systems Res. Forthcoming.Google Scholar
- (2007) The effects of self-construal and perceived control on privacy concerns. Twenty-Eighth Internat. Conf. Inform. Systems (Montreal, Quebec).Google Scholar
- (2022) Reflections on the 2021 Impact Award: Why privacy still matters. MIS Quart. 46(4):xx–xxxii.Google Scholar
- (2005) Consumers’ privacy concerns toward using location-based services: An exploratory framework and research proposal. Proc. 13th Eur. Conf. Inform. Systems Inform. Systems Rapidly Changing Econom. ECIS 2005 (AIS eLibrary, Atlanta).Google Scholar
- (2022) From contextualizing to context theorizing: Assessing context effects in privacy research. Management Sci. 68(10):7383–7401.Link, Google Scholar
- (2020) Validity concerns in research using organic data. J. Management 46(7):1257–1274.Crossref, Google Scholar
- (2012) Research note—Effects of individual self-protection, industry self-regulation, and government regulation on privacy concerns: A study of location-based services. Inform. Systems Res. 23(4):1342–1363.Link, Google Scholar
- (2021) Measuring brand favorability using large-scale social media data. Inform. Systems Res. 32(4):1128–1139.Link, Google Scholar
- (2016) Privacy nudges for mobile applications: Effects on the creepiness emotion and privacy attitudes. Proc. 19th ACM Conf. Comput. Supported Cooperative Work Soc. Comput. (ACM, New York), 1676–1690.Google Scholar

