Reidentification Risk in Panel Data: Protecting for k-Anonymity
Published Online:7 Oct 2022https://doi.org/10.1287/isre.2022.1169
References
- (2005) On k-anonymity and the curse of dimensionality. Proc. 31st Internat. Conf. Very Large Data Bases, 901–909.Google Scholar
- (2005) Approximation algorithms for k-anonymity. Proc. Internat. Conf. Database Theory.Google Scholar
- (1998) Marketing models of consumer heterogeneity. J. Econometrics 89(1–2):57–78.Crossref, Google Scholar
- (2022) Using deep learning to overcome privacy and scalability issues in customer data transfer. Marketing Sci. Forthcoming.Link, Google Scholar
- (2005) Data privacy through optimal k-anonymization. 21st Internat. Conf. Data Engrg., 217–228.Google Scholar
- (2010) Evaluating re-identification risks with respect to the HIPAA privacy rule. J. Amer. Medical Informatics Assoc. 17(2):169–177.Crossref, Google Scholar
- (1995) Automobile prices in market equilibrium. Econometrica 63(4):841–890.Crossref, Google Scholar
- (2003) Competitive price discrimination in a vertical channel using aggregate retail data. Management Sci. 49(9):1121–1138.Link, Google Scholar
- (1998) Logit demand estimation under competitive pricing behavior: An equilibrium framework. Management Sci., 44(11):1533–1547.Link, Google Scholar
- (2004) The recoverability of segmentation structure from store-level aggregate data. J. Marketing Res. 41(3):351–364.Crossref, Google Scholar
- (2001) Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. J. Marketing Res. 38(3):281–297.Crossref, Google Scholar
- (2008) Database paper—The IRI marketing data set. Marketing Sci. 27(4):745–748.Link, Google Scholar
- (2018) Targeting Mr. or Mrs. Smith: Modeling and leveraging intrahousehold heterogeneity in brand choice behavior. Marketing Sci. 37(4):631–648.Link, Google Scholar
- (1999) Commercial use of UPC scanner data: Industry and academic perspectives. Marketing Sci. 18(3):247–273.Link, Google Scholar
- (2007) Estimating disaggregate models using aggregate data through augmentation of individual choice. J. Marketing Res. 44(4):613–621.Crossref, Google Scholar
- (1980) Suppression methodology and statistical disclosure control. J. Amer. Statist. Assoc. 75(370):377–385.Crossref, Google Scholar
- (2013) Unique in the crowd: The privacy bounds of human mobility. Sci. Rep. 3:1376.Crossref, Google Scholar
- (2015) Unique in the shopping mall: On the reidentifiability of credit card metadata. Sci. 347(6221):536–539.Crossref, Google Scholar
- (2003) Record matching in data warehouses: A decision model for data consolidation. Oper. Res. 51(2):240–254.Link, Google Scholar
- (1998) A probabilistic decision model for entity matching in heterogeneous databases. Management Sci. 44(10):1379–1395.Link, Google Scholar
- (2005) Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Mining Knowledge Discovery 11(2):195–212.Crossref, Google Scholar
- (1986) Disclosure-limited data dissemination. J. Amer. Statist. Assoc. 81(393):10–18.Crossref, Google Scholar
- (1989) The risk of disclosure for microdata. J. Bus. Econom. Statist. 7(2):207–217.Google Scholar
- (2004) Disclosure risk vs. data utility: The RU confidentiality map as applied to topcoding. Chance 17(3):16–20.Crossref, Google Scholar
- (2006) Differential Privacy. Bugliesi M, Preneel B, Sassone V, Wegener I, eds. 33rd Internat. Colloquium Automata Languages Programming (Springer, Berlin/Heidelberg), 1-12.Google Scholar
- (2008) Protecting privacy using k-anonymity. J. Amer. Medical Informatics Assoc. 15(5):627–637.Crossref, Google Scholar
- (1969) A theory for record linkage. J. Amer. Statist. Assoc. 64(328):1183–1210.Crossref, Google Scholar
- (2017) Broadening marketing’s contribution to data privacy. J. Acad. Marketing Sci. 45(2):160–163.Crossref, Google Scholar
- (2020) They who must not be identified—Distinguishing personal from non-personal data under the GDPR. Internat. Data Privacy Law 10(1):11–36.Crossref, Google Scholar
- (2010) Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surveys 42(4):1–53.Crossref, Google Scholar
- (2009) Splitting a predictor at the upper quarter or third and the lower quarter or third. Amer. Statist. 63(1):1–8.Crossref, Google Scholar
- (2012) Shifts in privacy concerns. Amer. Econom. Rev. 102(3):349–353.Crossref, Google Scholar
- (2014) New York taxi details can be extracted from anonymised data, researchers say. The Guardian Online (June 27), https://www.theguardian.com/technology/2014/jun/27/new-york-taxi-details-anonymised-data-researchers-warn.Google Scholar
- (2007) Data Quality and Record Linkage Techniques, vol. 1 (Springer, New York).Google Scholar
- (2016) Drug detailing and doctors’ prescription decisions: The role of information content in the face of competitive entry. Marketing Sci. 35(6):915–933.Link, Google Scholar
- (2020) Protecting privacy when sharing and releasing data with multiple records per person. J. Assoc. Inform. Systems 21(6):1461–1485.Google Scholar
- (2012) A practical approximation algorithm for optimal k-anonymity. Data Mining Knowledge Discovery 25(1):134–168.Crossref, Google Scholar
- (1993) Measures of disclosure risk and harm. J. Official Statist. 9(2):313–331.Google Scholar
- (2005) Incognito: Efficient fulldomain k-anonymity. Proc. 2005 ACM SIGMOD Internat. Conf. Management Data, 49–60.Google Scholar
- (2006) Mondrian multidimensional k-anonymity. 22nd Internat. Conf. Data Engrg. (IEEE), 25.Google Scholar
- (2007) t-closeness: Privacy beyond k-anonymity and l-diversity. 2007 IEEE 23rd Internat. Conf. Data Engrg., 106–115.Google Scholar
- (2017) Anonymizing and sharing medical text records. Inform. Systems Res. 28(2):332–352.Link, Google Scholar
- (2006) Privacy protection in data mining: A perturbation approach for categorical data. Inform. Systems Res. 17(3):254–270.Link, Google Scholar
- (2011) Protecting privacy against record linkage disclosure: A bounded swapping approach for numeric data. Inform. Systems Res. 22(4):774–789.Link, Google Scholar
- (2013) Class-restricted clustering and microperturbation for data privacy. Management Sci. 59(4):796–812.Link, Google Scholar
- (2016) An empirical model of drug detailing: Dynamic competition and policy implications. Management Sci. 62(8):2321–2340.Link, Google Scholar
- (2012) The power of “like”: How brands reach (and influence) fans through social-media marketing. J. Advertising Res. 52(1):40–52.Crossref, Google Scholar
- (2006) l-diversity: Privacy beyond k-anonymity. 22nd Internat. Conf. Data Engrg. (IEEE), 24.Google Scholar
- (2008) Privacy: Theory meets practice on the map. 2008 IEEE 24th Internat. Conf. Data Engrg. (IEEE), 277–286.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
- (2004) Response modeling with nonrandom marketing-mix variables. J. Marketing Res. 41(4):467–478.Crossref, Google Scholar
- (2017) The role of data privacy in marketing. J. Acad. Marketing Sci. 45(2):135–155.Crossref, Google Scholar
- (2011) Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy. Statist. Surveys 5:1–29.Crossref, Google Scholar
- (2004) On the complexity of optimal k-anonymity. Proc. 23rd ACM SIGMOD-SIGACT-SIGART Sympos. Principles Database Systems (ACM), 223–228.Google Scholar
- (2009) Bayesian estimation of random‐coefficients choice models using aggregate data. J. Appl. Econometrics 24(3):490–516.Crossref, Google Scholar
- (2008) Robust de-anonymization of large sparse datasets. 2008 IEEE Sympos. Security Privacy, 111–125.Google Scholar
- Nergiz ME, Clifton C (2007) Thoughts on k-anonymization. Data and Knowledge Engrg. 63(3):622–645.Google Scholar
- (2005) Estimating risks of identification disclosure in microdata. J. Amer. Statist. Assoc. 100(472):1103–1112.Crossref, Google Scholar
- (2021) Sensor tower builds the ‘Nielsen’ of the app world. Forbes Online (April 9), https://www.forbes.com/sites/brucerogers/2021/04/09/sensor-tower-builds-the-nielsen-of-the-app-world/?sh=4c92f2472272.Google Scholar
- (1993) Statistical disclosure limitation. J. Official Statist. 9(2):461–468.Google Scholar
- (1998) Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report, SRI-CSL-98-04, Computer Science Laboratory, SRI International, Menlo Park, CA.Google Scholar
- (2017) Protecting customer privacy when marketing with second-party data. Internat. J. Res. Marketing 34(3):593–603.Crossref, Google Scholar
- (2018) A flexible method for protecting marketing data: An application to point-of-sale data. Marketing Sci. 37(1):153–171.Link, Google Scholar
- (2011) Information privacy research: An interdisciplinary review. Management Inform. Systems Quart. 35(4):989–1015.Crossref, Google Scholar
- (1996) Information privacy: Measuring individuals’ concerns about organizational practices. Management Inform. Systems Quart. 20(2):167–196.Crossref, Google Scholar
- (2000) Uniqueness of simple demographics in the US population. Technical report, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
- (2002a) Achieving k-anonymity privacy protection using generalization and suppression. Internat. J. Uncertainty Fuzziness Knowledge-Based Systems 10(5):571–588.Crossref, Google Scholar
- (2002b) k-anonymity: A model for protecting privacy. Internat. J. Uncertainty Fuzziness Knowledge-Based Systems 10(5):557–570.Crossref, Google Scholar
- (2014) Social networks, personalized advertising, and privacy controls. J. Marketing Res. 51(5):546–562.Crossref, Google Scholar
- Verizon (2019) 2019 Data breach investigations report. Accessed August 15, 2022, https://enterprise.verizon.com/resources/executivebriefs/2019-dbir-executive-brief.pdf.Google Scholar
- (2016) Marketing analytics for data-rich environments. J. Marketing 80(6):97–121.Crossref, Google Scholar
- (2021) Data analytics in a privacy-concerned world. J. Bus. Res. 122:915–925.Crossref, Google Scholar
- (2009) Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining. Decision Support Systems 48(1):133–140.Crossref, Google Scholar
- (2015) When to conduct probabilistic linkage vs. deterministic linkage? A simulation study. J. Biomedical Informatics 56:80–86.Crossref, Google Scholar

