Privacy-Preserving Data Fusion

Published Online:https://doi.org/10.1287/mksc.2023.0068

References

  • Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. Proc. 2016 ACM SIGSAC Conf. Comput. Comm. Security (Association for Computing Machinery, New York), 308–318.Google Scholar
  • Anand P, Lee C (2023) Using deep learning to overcome privacy and scalability issues in customer data transfer. Marketing Sci. 42(1):189–207.LinkGoogle Scholar
  • Ascarza E (2018) Retention futility: Targeting high-risk customers might be ineffective. J. Marketing Res. 55(1):80–98.CrossrefGoogle Scholar
  • Bradburn NM, Sudman S, Blair E, Locander W, Miles C, Singer E, Stocking C (1979) Improving Interview Method and Questionnaire Design: Response Effects to Threatening Questions in Survey Research (Jossey-Bass, San Francisco).Google Scholar
  • Bradlow ET, Zaslavsky AM (1999) A hierarchical latent variable model for ordinal data from a customer satisfaction survey with “no answer” responses. J. Amer. Statist. Assoc. 94(445):43–52.Google Scholar
  • Cai C, Sang Y, Tian H (2022) A multimodal differential privacy framework based on fusion representation learning. Connection Sci. 34(1):2219–2239.CrossrefGoogle Scholar
  • Carey C, Dick T, Epasto A, Javanmard A, Karlin J, Kumar S, Muñoz Medina A, et al. (2023) Measuring re-identification risk. Proc. ACM Management Data 1(2):149.CrossrefGoogle Scholar
  • Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 785–794.Google Scholar
  • Chen X, Simchi‐Levi D, Wang Y (2022) Privacy‐preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.LinkGoogle Scholar
  • Dankar FK, El Emam K, Neisa A, Roffey T (2012) Estimating the re-identification risk of clinical data sets. BMC Medical Informatics Decision Making 12(1):66.CrossrefGoogle Scholar
  • De Haan E, Verhoef PC, Wiesel T (2015) The predictive ability of different customer feedback metrics for retention. Internat. J. Res. Marketing 32(2):195–206.CrossrefGoogle Scholar
  • Dehghan M, Khosravian E, Golfar Z, Shahbazi H (2022) P2DF: Privacy-preserving data fusion protocol. 2022 12th Internat. Conf. Comput. Knowledge Engrg. (ICCKE) (IEEE, Piscataway, NJ), 211–218.Google Scholar
  • Ding W, Jing X, Yan Z, Yang LT (2019) A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Inform. Fusion 51:129–144.CrossrefGoogle Scholar
  • Dunn HL (1946) Record linkage. Amer. J. Public Health Nations Health 36(12):1412–1416.CrossrefGoogle Scholar
  • Dwork C, McSherry F, Nissim K, Smith A (2006a) Calibrating noise to sensitivity in private data analysis. Halevi S, Rabin T, eds. Theory of Cryptography. TCC 2006, Lecture Notes in Computer Science, vol. 3876 (Springer, Berlin), 265–284.CrossrefGoogle Scholar
  • Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006b) Our data, ourselves: Privacy via distributed noise generation. Annual Internat. Conf. Theory Appl. Cryptographic Techniques (Springer, Berlin, Heidelberg), 486–503.Google Scholar
  • Evans G, King G, Schwenzfeier M, Thakurta A (2023) Statistically valid inferences from privacy-protected data. Amer. Political Sci. Rev. 117(4):1275–1290.CrossrefGoogle Scholar
  • Feit EM, Beltramo MA, Feinberg FM (2010) Reality check: Combining choice experiments with market data to estimate the importance of product attributes. Management Sci. 56(5):785–800.LinkGoogle Scholar
  • Feit EM, Wang P, Bradlow ET, Fader PS (2013) Fusing aggregate and disaggregate data with an application to multiplatform media consumption. J. Marketing Res. 50(3):348–364.CrossrefGoogle Scholar
  • Gati NJ, Yang LT, Feng J, Nie X, Ren Z, Tarus SK (2021) Differentially private data fusion and deep learning framework for cyber–physical–social systems: State-of-the-art and perspectives. Inform. Fusion 76:298–314.CrossrefGoogle Scholar
  • Gilula Z, McCulloch RE, Rossi PE (2006) A direct approach to data fusion. J. Marketing Res. 43(1):73–83.CrossrefGoogle Scholar
  • Hassani S, Dackermann U, Mousavi M, Li J (2024) A systematic review of data fusion techniques for optimized structural health monitoring. Inform. Fusion 103:102136.CrossrefGoogle Scholar
  • Huang Q, Zhang J, Zeng Z, He D, Ye X, Chen Y (2023) PPDF-fedTMI: A federated learning-based transport mode inference model with privacy-preserving data fusion. Simulation Model. Practice Theory 129:102845.CrossrefGoogle Scholar
  • Kairouz P, Bonawitz K, Ramage D (2016) Discrete distribution estimation under local privacy. Internat. Conf. Machine Learn. (PMLR, New York), 2436–2444.Google Scholar
  • Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, Lima I Jr, et al. (2021) End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence 3(6):473–484.CrossrefGoogle Scholar
  • Kasiviswanathan SP, Lee HK, Nissim K, Raskhodnikova S, Smith A (2011) What can we learn privately? SIAM J. Comput. 40(3):793–826.CrossrefGoogle Scholar
  • Kim K, Tanuwidjaja HC (2021) Privacy-Preserving Deep Learning: A Comprehensive Survey (Springer, Singapore).CrossrefGoogle Scholar
  • Kingma DP, Welling M (2019) An introduction to variational autoencoders. Foundations Trends Machine Learn. 12(4):307–392.CrossrefGoogle Scholar
  • Klymenko A, Meisenbacher S, Lilova I, Matthes F (2024) Investigating the motivational factors influencing managerial decisions to adopt privacy-enhancing technologies. ECIS 2024 Proc. (AIS eLibrary).Google Scholar
  • Korganbekova M (2023) Balancing user privacy and personalization. Work in progress, Kellogg School of Management, Northwestern University, Evanston, IL.Google Scholar
  • Lemmens A, Gupta S (2020) Managing churn to maximize profits. Marketing Sci. 39(5):956–973.LinkGoogle Scholar
  • Li S, Schneider MJ, Yu Y, Gupta S (2023) Reidentification risk in panel data: Protecting for k-anonymity. Inform. Systems Res. 34(3):1066–1088.LinkGoogle Scholar
  • Lin T, Misra S (2022) Frontiers: The identity fragmentation bias. Marketing Sci. 41(3):433–440.LinkGoogle Scholar
  • Liu J, Lou J, Liu J, Xiong L, Pei J, Sun J (2021) Dealer: An end-to-end model marketplace with differential privacy. Proc. VLDB Endowment 14(6):957–969.CrossrefGoogle Scholar
  • Lobschat L, Mueller B, Eggers F, Brandimarte L, Diefenbach S, Kroschke M, Wirtz J (2021) Corporate digital responsibility. J. Bus. Res. 122:875–888.CrossrefGoogle Scholar
  • Malshe A, Colicev A, Mittal V (2020) How main street drives wall street: Customer (dis)satisfaction, short sellers, and abnormal returns. J. Marketing Res. 57(6):1055–1075.CrossrefGoogle Scholar
  • McCarthy DM, Oblander ES (2021) Scalable data fusion with selection correction: An application to customer base analysis. Marketing Sci. 40(3):459–480.LinkGoogle Scholar
  • Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. 2008 IEEE Sympos. Security Privacy (SP 2008) (IEEE, Piscataway, NJ), 111–125.Google Scholar
  • Neumann N, Tucker CE, Kaplan L, Mislove A, Sapiezynski P (2024) Data deserts and black boxes: The impact of socio-economic status on consumer profiling. Management Sci. 70(11):8003–8029.LinkGoogle Scholar
  • Prediger L, Loppi N, Kaski S, Honkela A (2022) d3p—A Python package for differentially-private probabilistic programming. Proc. Privacy Enhancing Tech. 2022(2):407–425.CrossrefGoogle Scholar
  • Rafieian O, Yoganarasimhan H (2021) Targeting and privacy in mobile advertising. Marketing Sci. 40(2):193–218.LinkGoogle Scholar
  • Rezende D, Mohamed S (2015) Variational inference with normalizing flows. Internat. Conf. Machine Learn. (PMLR, New York), 1530–1538.Google Scholar
  • Rogers R, Subramaniam S, Peng S, Durfee D, Lee S, Kancha SK, Sahay S, Ahammad P (2021) LinkedIn’s audience engagements API: A privacy preserving data analytics system at scale. J. Privacy Confidentiality 11(3).CrossrefGoogle Scholar
  • Ruggles S (2024) When privacy protection goes wrong: How and why the 2020 Census confidentiality program failed. J. Econom. Perspect. 38(2):201–226.CrossrefGoogle Scholar
  • Ruohonen J, Hjerppe K (2022) The GDPR enforcement fines at glance. Inform. Systems 106:101876.CrossrefGoogle Scholar
  • Schoenmueller V, Netzer O, Stahl F (2020) The polarity of online reviews: Prevalence, drivers and implications. J. Marketing Res. 57(5):853–877.CrossrefGoogle Scholar
  • Shelake VM, Shekokar N (2017) A survey of privacy preserving data integration. 2017 Internat. Conf. Electr. Electronics Comm. Comput. Optim. Techniques (ICEECCOT) (IEEE, Piscataway, NJ), 59–70.Google Scholar
  • Sisodia A, Burnap A, Kumar V (2025) Generative interpretable visual design: Using disentanglement for visual conjoint analysis. J. Marketing Res. 62(3):405–428.CrossrefGoogle Scholar
  • Steinke T (2022) Composition of differential privacy & privacy amplification by subsampling. Preprint, submitted October 26, https://arxiv.org/pdf/2210.00597.Google Scholar
  • Swait J, Andrews RL (2003) Enriching scanner panel models with choice experiments. Marketing Sci. 22(4):442–460.LinkGoogle Scholar
  • Sweeney L (1997) Weaving technology and policy together to maintain confidentiality. J. Law Medicine Ethics 25(2–3):98–110.CrossrefGoogle Scholar
  • Sweeney L (2000) Simple demographics often identify people uniquely. Health (San Francisco) 671(2000):1–34.Google Scholar
  • Sweeney L (2002) k-anonymity: A model for protecting privacy. Internat. J. Uncertainty Fuzziness Knowledge-Based Systems 10(05):557–570.CrossrefGoogle Scholar
  • Takagi S, Takahashi T, Cao Y, Yoshikawa M (2020) P3GM: Private high-dimensional data release via privacy preserving phased generative model. 2021 IEEE 37th Internat. Conf. Data Engineering (ICDE) (IEEE, Piscataway, NJ), 169–180.Google Scholar
  • Tian Z, Dew R, Iyengar R (2024) Mega or micro? Influencer selection using follower elasticity. J. Marketing Res. 61(3):472–495.CrossrefGoogle Scholar
  • Turjeman D, Feinberg FM (2024) When the data are out: Measuring behavioral changes following a data breach. Marketing Sci. 43(2):440–461.LinkGoogle Scholar
  • Unger M, Shapira B, Rokach L, Livne A (2018) Inferring contextual preferences using deep encoder-decoder learners. New Rev. Hypermedia Multimedia 24(3):262–290.CrossrefGoogle Scholar
  • U.S. Census Bureau (2021) Disclosure avoidance for the 2020 Census: An introduction. Report, U.S. Census Bureau, Washington, DC.Google Scholar
  • Verhoef PC (2003) Understanding the effect of customer relationship management efforts on customer retention and customer share development. J. Marketing 67(4):30–45.CrossrefGoogle Scholar
  • Wang Q, Yang K (2024) Privacy-preserving data fusion for traffic state estimation: A vertical federated learning approach. Transportation Sci. 168 (104743).Google Scholar
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