Personalized Privacy Preservation in Consumer Mobile Trajectories

Published Online:https://doi.org/10.1287/isre.2023.1227

References

  • Abul O, Bonchi F, Nanni M (2008) Never walk alone: Uncertainty for anonymity in moving objects databases. IEEE 24th Internat. Conf. Data Engrg, 2008 (IEEE, Piscataway, NJ), 376–385.Google Scholar
  • Ahmad MW, Mourshed M, Rezgui Y (2017) Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 147:77–89.CrossrefGoogle Scholar
  • Andrews M, Luo X, Fang Z, Ghose A (2016) Mobile ad effectiveness: Hyper-contextual targeting with crowdedness. Marketing Sci. 35(2):218–233.LinkGoogle Scholar
  • Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquitous Comput. 7(5):275–286.CrossrefGoogle Scholar
  • Bhakta A, Kim Y, Cole P (2021) Comparing machine learning-centered approaches for forecasting language patterns during frustration in early childhood. Preprint, submitted October 29, https://arxiv.org/abs/2110.15778.Google Scholar
  • Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst. Appl. 38(12):14609–14623.CrossrefGoogle Scholar
  • Brauer A, Mäkinen V, Forsch A, Oksanen J, Haunert J-H (2022) My home is my secret: Concealing sensitive locations by context-aware trajectory truncation. Internat. J. Geogr. Inform. Sci. 36(12):2496–2524.CrossrefGoogle Scholar
  • Breiman L (2001) Random forests. Machine Learn. 45:5–32.CrossrefGoogle Scholar
  • Chandra S, Verma S, Lim WM, Kumar S, Donthu N (2022) Personalization in personalized marketing: Trends and ways forward. Psych. Marketing 39(8):1529–1562. 10.1002/mar.21670CrossrefGoogle Scholar
  • Chen P, Niu A, Wei J, Liu D (2019) Air pollutant prediction: Comparisons between LSTM, light GBM and random forest. Geophys. Res. Abstr. 21:EGU2019–3121–1.Google Scholar
  • Chen R, Fung BCM, Mohammed N, Desai BC, Wang K (2013) Privacy-preserving trajectory data publishing by local suppression. Inform. Sci. 231:83–97.CrossrefGoogle Scholar
  • Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, 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
  • Chow C-Y, Mokbel MF (2011) Trajectory privacy in location-based services and data publication. SIGKDD Explor. 13(1):19–29.CrossrefGoogle Scholar
  • Coope ID, Price CJ (2001) On the convergence of grid-based methods for unconstrained optimization. SIAM J. Optim. 11(4):859–869.CrossrefGoogle Scholar
  • Cunha M, Mendes R, Vilela JP (2021) A survey of privacy-preserving mechanisms for heterogeneous data types. Comput. Sci. Rev. 41:100403.CrossrefGoogle Scholar
  • De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4):1253–1278.CrossrefGoogle Scholar
  • Dwork C, Lei J (2009) Differential privacy and robust statistics. Proc. 41st Annual ACM Sympos. Theory Comput. (ACM, New York), 371–380.Google Scholar
  • Eagle N, Pentland AS (2009) Eigenbehaviors: Identifying structure in routine. Behav. Ecol. Sociobiol. 63(7):1057–1066.CrossrefGoogle Scholar
  • Fernández-Delgado M, Cernadas E, Barro S (2014) Do we need hundreds of classifiers to solve real world classification problems? J. Machine Learn. Res. 15:3133–3181.Google Scholar
  • Fiore M, Katsikouli P, Zavou E, Cunche M, Fessant F, Le Hello D, Aivodji UM, Olivier B, Quertier T, Stanica R (2020) Privacy in trajectory micro-data publishing: A survey. Trans. Data Privacy, IIIA-CSIC. 13:91–149.Google Scholar
  • Friedman J, Hastie T, Tibshirani R (2001) The Elements of Statistical Learning, vol. 1. Springer Series in Statistics (Springer, New York).Google Scholar
  • Gao S, Ma J, Sun C, Li X (2014) Balancing trajectory privacy and data utility using a personalized anonymization model. J. Netw. Comput. Appl. 38:125–134.CrossrefGoogle Scholar
  • Gardete PM, Bart Y (2018) Tailored cheap talk: The effects of privacy policy on ad content and market outcomes. Marketing Sci. 37(5):733–752.LinkGoogle Scholar
  • Ghose A (2017) TAP: Unlocking the Mobile Economy (MIT Press, Cambridge, MA).CrossrefGoogle Scholar
  • Ghose A, Li B, Liu S (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.LinkGoogle Scholar
  • Ghose A, Li B, Macha M, Sun C, Foutz NZ (2020) Trading privacy for public good: How did America react during COVID-19? Preprint submitted, June 10, https://dx.doi.org/10.2139/ssrn.3624069.Google Scholar
  • Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779.CrossrefGoogle Scholar
  • Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class AdaBoost. Stat. Interface. 2(3):349–360.CrossrefGoogle Scholar
  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.CrossrefGoogle Scholar
  • Huang J-C, Tsai Y-C, Wu P-Y, Lien Y-H, Chien C-Y, Kuo C-F, Hung J-F, Chen S-C, Kuo C-H (2020) Predictive modeling of blood pressure during hemodialysis: A comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Comput. Methods Programs Biomed. 195:105536.CrossrefGoogle Scholar
  • Huo Z, Meng X, Hu H, Huang Y (2012) You can walk alone: Trajectory privacy-preserving through significant stays protection. Internat. Conf. Database Systems Adv. Appl. (Springer, New York), 351–366.Google Scholar
  • Hwang R-H, Hsueh Y-L, Chung H-W (2013) A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Trans. Serv. Comput. 7(2):126–139.CrossrefGoogle Scholar
  • Jhaveri S, Khedkar I, Kantharia Y, Jaswal S (2019) Success prediction using random forest, CatBoost, XGBoost and AdaBoost for Kickstarter campaigns. Proc. 2019 3rd Internat. Conf. Comput. Methodol. Comm. (IEEE, Piscataway, NJ), 1170–1173.Google Scholar
  • Jiang H, Li J, Zhao P, Zeng F, Zhu X, Iyengar A (2022) Location privacy-preserving mechanisms in location-based services: A comprehensive survey. ACM Comput. Surv. 54(1):1–36.Google Scholar
  • Jin F, Hua W, Francia M, Chao P, Orlowska M, Zhou X (2022) A survey and experimental study on privacy-preserving trajectory data publishing. IEEE Trans. Knowledge Data Engrg. 35(6):5577–5596.CrossrefGoogle Scholar
  • Katsomallos M, Tzompanaki K, Kotzinos D (2019) Privacy, space, and time: A survey on privacy-preserving continuous data publishing. J. Spatial Inform. Sci. 19:57–103.Google Scholar
  • Kelsey (2018) US Local Mobile Local Social Ad Forecast. Accessed May 11, 2022, https://shop.biakelsey.com/product/2018-u-s-local-mobile-local-social-ad-forecast/.Google Scholar
  • Komishani EG, Abadi M, Deldar F (2016) PPTD: Preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression. Knowledge-Based Systems 94:43–59.CrossrefGoogle Scholar
  • Li C, Shirani-Mehr H, Yang X (2007a) Protecting individual information against inference attacks in data publishing. Internat. Conf. Database Systems Adv. Appl. (Springer, New York), 422–433.Google Scholar
  • Li N, Li T, Venkatasubramanian S (2007b) t-closeness: Privacy beyond k-anonymity and l-diversity. IEEE 23rd Internat. Conf. Data Engrg. (IEEE, Piscataway, NJ).Google Scholar
  • Li S, Shen H, Sang Y (2020) A survey of privacy-preserving techniques on trajectory data. Shen H, Sang Y, eds. Parallel Architectures, Algorithms and Programming (Springer Singapore, Singapore), 461–476.CrossrefGoogle Scholar
  • Li X-B, Qin J (2017) Anonymizing and sharing medical text records. Inform. Systems Res. 28(2):332–352.LinkGoogle Scholar
  • Li X-B, Sarkar S (2013) Class-restricted clustering and microperturbation for data privacy. Management Sci. 59(4):796–812.LinkGoogle Scholar
  • Li X-B, Sarkar S (2014) Digression and value concatenation to enable privacy-preserving regression. MIS Quart. 38(3):679–698.CrossrefGoogle Scholar
  • Li X-B, Sarker S (2011) Protecting privacy against record linkage disclosure: A bounded swapping approach for numeric data. Inform. Systems Res. 22(4):774–789.LinkGoogle Scholar
  • Luo X, Andrews M, Fang Z, Phang CW (2014) Mobile targeting. Management Sci. 60(7):1738–1756.LinkGoogle Scholar
  • Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M (2006) ℓ-Diversity: Privacy Beyond κ-Anonymity (IEEE, Piscataway, NJ), 24.Google Scholar
  • Mahdavifar S, Deldar F, Mahdikhani H (2022) Personalized privacy-preserving publication of trajectory data by generalization and distortion of moving points. J. Netw. Systems Management 30(10).Google Scholar
  • Menon S, Sarkar S (2016) Privacy and big data: Scalable approaches to sanitize large transactional databases for sharing. MIS Quart. 40(4):963–982.CrossrefGoogle Scholar
  • Murakami T, Hamada K, Kawamoto Y, Hatano T (2019) Privacy-preserving multiple tensor factorization for synthesizing large-scale location traces with cluster-specific features. Preprint, submitted November 11, https://arxiv.org/abs/1911.04226.Google Scholar
  • Papoulis A (1984) Probability Random Variables, and Stochastic Processes.Google Scholar
  • Pappalardo L, Rinzivillo S, Simini F (2016) Human mobility modelling: Exploration and preferential return meet the gravity model. Procedia Comput. Sci. 83:934–939.CrossrefGoogle Scholar
  • Pascual S, Bonafonte A (2016) Multi-output RNN-LSTM for multiple speaker speech synthesis and adaptation. Proc. 2016 24th Eur. Signal Processing Conf. (IEEE, Piscataway, NJ), 2325–2329.Google Scholar
  • Pelekis N, Gkoulalas-Divanis A, Vodas M, Kopanaki D, Theodoridis Y (2011) Privacy-aware querying over sensitive trajectory data. Proc. 20th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 895–904.Google Scholar
  • Pellungrini R, Pappalardo L, Pratesi F, Monreale A (2018) A data mining approach to assess privacy risk in human mobility data. ACM Trans. Intell. Syst. Technol. 9(3):31.CrossrefGoogle Scholar
  • Pew (2018) Americans’ complicated feelings about social media in an era of privacy concerns. Accessed May 11, 2022, https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/.Google Scholar
  • Primault V, Boutet A, Ben Mokhtar S, Brunie L (2019) The long road to computational location privacy: A survey. IEEE Comm. Surv. Tutor. 21(3):2772–2793.CrossrefGoogle Scholar
  • Qiu G, Guo D, Shen Y, Tang G, Chen S (2021) Mobile semantic-aware trajectory for personalized location privacy preservation. IEEE Internet Things J. 8(21):16165–16180.CrossrefGoogle Scholar
  • Rafieian O, Yoganarasimhan H (2021) Targeting and privacy in mobile advertising. Marketing Sci. 40(2):193–218.LinkGoogle Scholar
  • Rao J, Gao S, Kang Y, Huang Q (2020) LSTM-TrajGAN: A deep learning approach to trajectory privacy protection. Preprint, submitted June 14, https://arxiv.org/abs/2006.10521.Google Scholar
  • Sagi O, Rokach L (2018) Ensemble learning: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8:e1249.CrossrefGoogle Scholar
  • Said N, Mouazen AM (2017) Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensor. 17:2428–2450.CrossrefGoogle Scholar
  • Sarkar M, De Bruyn A (2021) LSTM response models for direct marketing analytics: Replacing feature engineering with deep learning. J. Interact. Marketing 53:80–95. 10.1016/j.intmar.2020.07.002CrossrefGoogle Scholar
  • Taylor K, Silver L (2019) Smartphone ownership is growing rapidly around the world, but not always equally. Accessed May 11, 2022, http://www.pewglobal.org/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/.Google Scholar
  • Terrovitis M, Poulis G, Mamoulis N, Skiadopoulos S (2017) Local suppression and splitting techniques for privacy preserving publication of trajectories. IEEE Trans. Knowledge Data Engrg. 29(7):1466–1479.CrossrefGoogle Scholar
  • Thompson SA, Warzel C (2019) Twelve million phones, one data set, zero privacy. Accessed May 11, 2022, https://www.nytimes.com/interactive/2019/12/19/opinion/location-tracking-cell-phone.html.Google Scholar
  • Tong S, Luo X, Xu B (2020) Personalized mobile marketing strategies. J. Acad. Marketing Sci. 48:64–87.CrossrefGoogle Scholar
  • Tucker CE (2013) Social networks, personalized advertising, and privacy controls. J. Marketing Res. 50(5):546–562.CrossrefGoogle Scholar
  • Valentino-Devries J, Singer N, Keller MH, Krolik A (2018) Your apps know where you were last night, and they’re not keeping it secret. Accessed May 11, 2022, https://www.nytimes.com/interactive/2018/12/10/business/location-data-privacy-apps.html?smid=re-nytimes.Google Scholar
  • Wang D, Pedreschi D, Song C, Giannotti F, Barabasi A-L (2011) Human mobility, social ties, and link prediction. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1100–1108.Google Scholar
  • Wang K, Fung BCM, Philip SY (2007) Handicapping attacker’s confidence: An alternative to k-anonymization. Knowledge Inform. Systems 11(3):345–368.CrossrefGoogle Scholar
  • Wedel M, Kannan PK (2016) Marketing analytics for data-rich environments. J. Marketing 80(6):97–121.CrossrefGoogle Scholar
  • Weinberg AI, Last M (2019) Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification. J. Big Data. 61(23).Google Scholar
  • Williams NE, Timothy A Thomas MD, Eagle N, Dobra A (2015) Measures of human mobility using mobile phone records enhanced with GIS data. PLoS One. 10(7):e0133630.CrossrefGoogle Scholar
  • Yang D, Qu B, Cudre-Mauroux P (2018) Privacy-preserving social media data publishing for personalized ranking-based recommendation. IEEE Trans. Knowledge Data Engrg. 31(3):507–520.Google Scholar
  • Yao L, Chen Z, Hu H, Wu G, Wu B (2021) Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity. Distrib. Parallel Databases 39(3):785–811.CrossrefGoogle Scholar
  • Yarovoy R, Bonchi F, Lakshmanan LVS, Wang WH (2009) Anonymizing moving objects: How to hide a mob in a crowd? Proc. 12th Internat. Conf. Extending Database Tech. Adv. Database Tech. (ACM, New York), 72–83.Google Scholar
  • Zheng Yu, Xie X, Ma W-Y (2010) Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2):32–39.Google Scholar
  • Zhou Y, Lu S, Ding M (2020) Contour-as-face framework: A method to preserve privacy and perception. J. Marketing Res. 57(4):617–639.CrossrefGoogle Scholar
  • Zhou Y, Chang F-J, Chang L-C, Kao I-F, Wang Y-S (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J. Clean. Prod. 209:134–145.CrossrefGoogle Scholar
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