Abstract
Personal data has been described as the “the new oil of the Internet.” The global data monetization market is projected to increase to USD 6.1bn by 2025, and the success of giants like Facebook or Google speaks for itself. Almost all companies create, store, share and/or use personal data i.e. information from or about individuals. While the current assumption is that data subjects voluntarily share their data in exchange for a “free” service, the awareness of the value of personal data and data sovereignty is growing amongst consumers, businesses, and regulators alike. However, there is currently no consensus on which factors influence the value of personal data and how personal data should be priced regarding self-determination and data sovereignty. With this narrative review, we answer the following research question: Which factors influence the pricing of personal data? We show that research on the subject is diverse and that there is no consensus on the optimal pricing mechanism. We identify individual privacy and risk preferences, informational self-determination, sensitivity of data and data volume and inferability as most prevalent influence factors. We underline the need to establish ways for data owners to exercise data sovereignty and informed consent about data usage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The category “Data Market Model, Technical” refers to papers focusing on algorithms and technical solutions for data markets.
- 2.
Willingness to pay (WTP) refers to the maximum amount of money an individual would be willing to pay to secure a specific change, while willingness to accept (WTA) refers to the minimum amount a person would be willing to accept to forego said change [34].
References
Kuneva, M.: Keynote speech. Roundtable on online data collection, targeting and profiling, 31 March 2009
MarketsandMarkets: Data Monetization Market (2020). https://www.marketsandmarkets.com/Market-Reports/data-monetization-market-127405959.html. Accessed 23 Sep 2020
Spiekermann, S., Korunovska, J.: Towards a value theory for personal data. J. Inf. Technol. (2017). https://doi.org/10.1057/jit.2016.4
Feijóo, C., Gómez-Barroso, J.L., Voigt, P.: Exploring the economic value of personal information from firms’ financial statements. Int. J. Inf. Manag. (2014). https://doi.org/10.1016/j.ijinfomgt.2013.12.005
OECD: Exploring the Economics of Personal Data: A Survey of Methodologies for Measuring Monetary Value (2013). https://doi.org/10.1787/5k486qtxldmq-en
Sidgman, J., Crompton, M.: Valuing personal data to foster privacy: a thought experiment and opportunities for research. J. Inf. Syst. (2016). https://doi.org/10.2308/isys-51429
Acquisti, A., Taylor, C., Wagman, L.: The economics of privacy. J. Econ. Lite. (2016). https://doi.org/10.1257/jel.54.2.442
European Commission: EU data protection rules (2020). https://ec.europa.eu/info/law/law-topic/data-protection/eu-data-protection-rules_en. Accessed 27 July 2020
Madiega, T.: Digital sovereignty for Europe. European Union (2020)
European Commission: A European Strategy for data (2021). https://digital-strategy.ec.europa.eu/en/policies/strategy-data. Accessed 13 Jan 2022
Schwartz, P.M.: Property, privacy, and personal data. Harv. Law Rev. (2004). https://doi.org/10.2307/4093335
Sindermann, C., Kuss, D.J., Throuvala, M.A., Griffiths, M.D., Montag, C.: Should we pay for our social media/messenger applications? Preliminary data on the acceptance of an alternative to the current prevailing data business model. Front. Psychol. (2020). https://doi.org/10.3389/fpsyg.2020.01415
Currie, C.S.M., Dokka, T., Harvey, J., Strauss, A.K.: Future research directions in demand management. J. Revenue Pricing Manag. (2018). https://doi.org/10.1057/s41272-018-0139-z
Malgieri, G., Custers, B.: Pricing privacy–the right to know the value of your personal data. Comput. Law Secur. Rev. (2018). https://doi.org/10.1016/j.clsr.2017.08.006
Fricker, S.A., Maksimov, Y.V.: Pricing of data products in data marketplaces. In: Ojala, A., Holmström Olsson, H., Werder, K. (eds.) ICSOB 2017. LNBIP, vol. 304, pp. 49–66. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69191-6_4
Wdowin, J., Diepeveen, S.: The value of data: literature review, Cambrige, UK (2020)
Cook, D.J., Mulrow, C.D., Haynes, R.B.: Systematic reviews: synthesis of best evidence for clinical decisions. Ann. Intern. Med. (1997). https://doi.org/10.7326/0003-4819-126-5-199703010-00006
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ (Clin. Res. Ed.) (2009). https://doi.org/10.1136/bmj.b2535
Elo, S., Kyngäs, H.: The qualitative content analysis process. J. Adv. Nurs. 62, 107–115 (2008)
Niyato, D., Alsheikh, M.A., Wang, P., Kim, D.I., Han, Z.: Market model and optimal pricing scheme of big data and Internet of Things (IoT). In: 2016 IEEE International Conference on Communications (ICC). 2016 IEEE International Conference on Communications (ICC), pp. 1–6 (2016). https://doi.org/10.1109/ICC.2016.7510922
Wang, W., Ying, L., Zhang, J.: The value of privacy: strategic data subjects, incentive mechanisms and fundamental limits. In: Proceedings of the SIGMETRICS 2016: Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science (2016)
Jiao, Y., Wang, P., Niyato, D., Abu Alsheikh, M., Feng, S.: Profit maximization auction and data management in big data markets. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC). 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2017). https://doi.org/10.1109/WCNC.2017.7925760
Bataineh, A.S., Mizouni, R., Barachi, M.E., Bentahar, J.: Monetizing personal data: a two-sided market approach. Procedia Comput. Sci. (2016). https://doi.org/10.1016/j.procs.2016.04.211
Xu, L., Jiang, C., Qian, Y., Zhao, Y., Li, J., Ren, Y.: Dynamic privacy pricing: a multi-armed bandit approach with time-variant rewards. IEEE Trans. Inf. Forensics Secur. (2017). https://doi.org/10.1109/TIFS.2016.2611487
Nget, R., Cao, Y., Yoshikawa, M.: How to balance privacy and money through pricing mechanism in personal data market (2017)
Yang, J., Xing, C.: Personal data market optimization pricing model based on privacy level. Information 10, 123 (2019)
Di Vimercati, S.D.C., Foresti, S., Livraga, G., Samarati, P.: Toward owners’ control in digital data markets. IEEE Syst. J. 1–8 (2020). https://doi.org/10.1109/JSYST.2020.2970456
Mahmoodi, J., et al.: Internet users’ valuation of enhanced data protection on social media: which aspects of privacy are worth the most? Front. Psychol. (2018). https://doi.org/10.3389/fpsyg.2018.01516
Bauer, M., et al.: Using crash outcome data evaluation system (CODES) to examine injury in front vs. rear-seated infants and children involved in a motor vehicle crash in New York State. Inj. Epidemiol. 8, 1–10 (2021)
Frik, A., Gaudeul, A.: A measure of the implicit value of privacy under risk. J. Consum. Mark. (2020). https://doi.org/10.1108/JCM-06-2019-3286
Benndorf, V., Normann, H.-T.: The willingness to sell personal data. DICE discussion paper, vol. 143. DICE, Düsseldorf (2014)
Staiano, J., Oliver, N., Lepri, B., de Oliveira, R., Caraviello, M., Sebe, N.: Money walks: a human-centric study on the economics of personal mobile data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 13 September 2014, pp. 583–594 (2014). https://doi.org/10.1145/2632048.2632074
Danezis, G., Lewis, S., Anderson, R.J.: How much is location privacy worth? In: WEIS
Hanemann, W.M.: Willingness to pay and willingness to accept: how much can they differ? Am. Econ. Rev. 81, 635–647 (1991)
Acquisti, A., John, L.K., Loewenstein, G.: What is privacy worth? J. Leg. Stud. (2013). https://doi.org/10.1086/671754
Winegar, A.G., Sunstein, C.R.: How much is data privacy worth? A preliminary investigation. SSRN J. (2019). https://doi.org/10.2139/ssrn.3413277
Grossklags, J., Acquisti, A.: When 25 cents is too much: an experiment on willingness-to-sell and willingness-to- protect personal information. In: Proceedings of the Workshop on the Economics of Information Security Proceedings 2007 (2007)
Nielsen, A.: Measuring lay reactions to personal data markets. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021) (2021). https://doi.org/10.1145/3461702.3462582
Biswas, S., Jung, K., Palamidessi, C.: An incentive mechanism for trading personal data in data markets. In: Cerone, A., Ölveczky, P.C. (eds.) ICTAC 2021. LNCS, vol. 12819, pp. 197–213. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85315-0_12
Cockcroft, S., Rekker, S.: The relationship between culture and information privacy policy. Electron. Mark. 26(1), 55–72 (2015). https://doi.org/10.1007/s12525-015-0195-9
Hacker, P., Petkova, B.: Reining in the big promise of big data: transparency, inequality, and new regulatory frontiers. Northwestern J. Technol. Intellect. Prop. 15(1), 6–42 (2017)
Holt, T.J., Smirnova, O., Chua, Y.T.: Exploring and estimating the revenues and profits of participants in stolen data markets. Deviant Behav. (2016). https://doi.org/10.1080/01639625.2015.1026766
Jentzsch, N.: Monetarisierung der privatsphäre: welchen preis haben persönliche daten? DIW Wochenbericht (2014)
Perera, C., Ranjan, R., Wang, L.: End-to-end privacy for open big data markets. IEEE Cloud Comput. 2(4), 44–53 (2015). https://doi.org/10.1109/MCC.2015.78
Raskar, R., Vepakomma, P., Swedish, T., Sharan, A.: Data markets to support AI for all: pricing, valuation and governance (2019)
Choi, J.P., Jeon, D.-S., Kim, B.-C.: Privacy and personal data collection with information externalities. J. Public Econ. (2019). https://doi.org/10.1016/j.jpubeco.2019.02.001
Dimakopoulos, P.D., Sudaric, S.: Privacy and platform competition. Int. J. Ind. Organ. (2018). https://doi.org/10.1016/j.ijindorg.2018.01.003
Gkatzelis, V., Aperjis, C., Huberman, B.A.: Pricing private data. Electron. Mark. 25(2), 109–123 (2015). https://doi.org/10.1007/s12525-015-0188-8
Li, X.-B., Raghunathan, S.: Pricing and disseminating customer data with privacy awareness. Decis. Support Syst. (2014). https://doi.org/10.1016/j.dss.2013.10.006
Oh, H., Park, S., Lee, G.M., Choi, J.K., Noh, S.: Competitive data trading model with privacy valuation for multiple stakeholders in IoT data markets. IEEE Internet Things J. 7(4), 3623–3639 (2020). https://doi.org/10.1109/JIOT.2020.2973662
Radhakrishnan, A., Das, S.: Data markets for smart grids: an introduction. In: 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). 2018 IEEE Innovative Smart Grid Technologies–Asia (ISGT Asia), pp. 1010–1015 (2018). https://doi.org/10.1109/ISGT-Asia.2018.8467818
Spiekermann, S., Acquisti, A., Böhme, R., Hui, K.-L.: The challenges of personal data markets and privacy. Electron. Mark. 25(2), 161–167 (2015). https://doi.org/10.1007/s12525-015-0191-0
Spiekermann, S., Novotny, A.: A vision for global privacy bridges: technical and legal measures for international data markets Comput. Law Secur. Rev. (2015). https://doi.org/10.1016/j.clsr.2015.01.009
Tian, L., Li, J., Li, W., Ramesh, B., Cai, Z.: Optimal contract-based mechanisms for online data trading markets. IEEE Internet Things J. 6(5), 7800–7810 (2019). https://doi.org/10.1109/JIOT.2019.2902528
Balazinska, M., Howe, B., Suciu, D.: Data markets in the cloud: an opportunity for the database community. Proc. VLDB Endow. 5(12), 1962–1965 (2011)
Li, C., Li, D.Y., Miklau, G., Suciu, D.: A theory of pricing private data. ACM Trans. Database Syst. (2014). https://doi.org/10.1145/2691190.2691191
Mehta, S., Dawande, M., Janakiraman, G., Mookerjee, V.: How to sell a dataset? Pricing policies for data monetization. SSRN J. (2019). https://doi.org/10.2139/ssrn.3333296
Niu, C., Zheng, Z., Wu, F., Tang, S., Gao, X., Chen, G.: Online pricing with reserve price constraint for personal data markets (2019)
Niu, C., Zheng, Z., Wu, F., Tang, S., Gao, X., Chen, G.: ERATO: trading noisy aggregate statistics over private correlated data. IEEE Trans. Knowl. Data Eng. (2019). https://doi.org/10.1109/TKDE.2019.2934100
Shen, Y., Guo, B., Duan, X., Dong, X., Zhang, H.: A pricing model for big personal data. Tsinghua Sci. Technol. 21(5), 482–490 (2016). https://doi.org/10.1109/TST.2016.7590317
Shen, Y., et al.: Pricing personal data based on data provenance. Appl. Sci. Basel 9(12) (2019). https://doi.org/10.3390/app9163388
Zhang, Z., Song, W., Shen, Y.: A reasonable data pricing mechanism for personal data transactions with privacy concern. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds.) APWeb-WAIM 2021. LNCS, vol. 12859, pp. 64–71. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85899-5_5
Hann, I.-H., Hui, K.-L., Lee, S.-Y.T., Png, I.P.L.: Overcoming online information privacy concerns: an information-processing theory approach. J. Manag. Inf. Syst. (2007). https://doi.org/10.2753/MIS0742-1222240202
Lim, S., Woo, J., Lee, J., Huh, S.-Y.: Consumer valuation of personal information in the age of big data. J. Assoc. Inf. Sci. Technol. (2018). https://doi.org/10.1002/asi.23915
Schomakers, E.-M., Lidynia, C., Ziefle, M.: All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity. Electron. Mark. 30(3), 649–665 (2020). https://doi.org/10.1007/s12525-020-00404-9
Coyle, D., Diepeveen, S., Wdowin, J., Kay, L., Tennison, J.: The value of data: policy implications, Cambrige, UK (Februaray 2020). https://www.nuffieldfoundation.org/project/valuing-data-foundations-for-data-policy
Rose, J., Rehse, O., Robe, B.: The value of our digital identity (2012). https://www.bcg.com/publications/2012/digital-economy-consumer-insight-value-of-our-digital-identity.aspx. Accessed 29 Apr 2020
Acknowledgements
This research was partially funded by the German Federal Ministry of Education and Research (BMBF) within the scope of the research project DaWID (Platform for value determination and self-determined data release; funding reference number: 16SV8383).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix - Overview of Included Papers
Appendix - Overview of Included Papers
# | Paper | Classification | Key issue explored |
|---|---|---|---|
13 | Feijóo et al. [4] | Case Study | Case study on estimation of personal data |
18 | Hacker & Petkova [41] | Case Study | Case study on active choice of using data as currency |
20 | Holt et al. [42] | Case Study | Case study on value of data in stolen data markets |
21 | Jentzsch [43] | Commentary | Commentary on the difficulties of valuing personal data |
37 | Perera et al. [44] | Commentary | Commentary on the challenges of privacy protection in IoT |
39 | Raskar et al. [45] | Commentary | Commentary on challenges of data pricing and data markets |
44 | Sidgman & Crompton [6] | Commentary | Theoretical commentary on current challenges and research opportunities |
4 | Bataineh et al. [23] | Data Market Model | Two-sided data market model with experimental comparison based on real life data set |
8 | Choi et al. [46] | Data Market Model | Theoretical data market model with consumer consent for data collection |
12 | Dimakopoulos & Sudaric [47] | Data Market Model | Theoretical data market with platform competition |
16 | Gkatzelis et al. [48] | Data Market Model | Theoretical data market model for unbiased data samples |
22 | Jiao et al. [22] | Data Market Model | Data market model with Bayesian profit maximization auction |
23 | Lei Xu et al. [24] | Data Market Model | Theoretical data market model with privacy and learning policies in a multi-armed bandit model |
24 | Li & Raghunathan [49] | Data Market Model | Data market model when purpose of data use is unclear |
34 | Niyato et al. [20] | Data Market Model | Theoretical data market model for optimal big data pricing with simulation |
36 | Oh et al. [50] | Data Market Model | Theoretical data market model between broker and service provider under profit maximization and respect for privacy protection and valuation |
38 | Radhakrishnan & Das [51] | Data Market Model | Theoretical data market model for smart grid data |
45 | Spiekermann et al. [52] | Data Market Model | Theoretical data market model focusing on challenges of personal data markets |
46 | Spiekermann & Novotny [53] | Data Market Model | Theoretical data market model focusing on operating principles |
49 | Tian et al. [54] | Data Market Model | Theoretical data market model based on optimal contract-based mechanisms |
50 | Wang et al. [21] | Data Market Model | Theoretical data market model with data owners exhibiting informed consent in a Nash equilibrium with a non-trusted data collector |
3 | Balazinska et al. [55] | Data Market Model, Technical | Technical data market model with query-based pricing |
11 | De Capitani Di Vimercati et al. [27] | Data Market Model, Technical | Technical data market model focusing on including privacy issues in a cloud setting |
25 | Li et al. [56] | Data Market Model, Technical | Technical data market model with query-based pricing |
30 | Nget et al.[25] | Data Market Model, Technical | Technical market model and simulation of query-based pricing mechanism |
53 | Yang & Xing [26] | Data Market Model, Technical | Algorithm for personal data pricing with multi-level privacy division |
7 | Biswas et al. [39] | Data Pricing Model | Theoretical model to induce data provider to accurately report privacy price within differential privacy |
29 | Mehta et al. [57] | Data Pricing Model | Theoretical data pricing model with price-quantity schedule and approximation scheme for data seller |
32 | Niu et al.[58] | Data Pricing Model | Technical pricing model for trading aggregate statistics over private correlated data |
33 | Niu et al. [59] | Data Pricing Model | Algorithm for personal data pricing with reverse price constraint |
42 | Shen et al. [60] | Data Pricing Model | Data pricing model for Big Personal Data based on tuple granularity |
43 | Shen et al. [61] | Data Pricing Model | Data pricing model based on data provenance |
54 | Zhang et al. [62] | Data Pricing Model | Data pricing with privacy concern introducing privacy cost concept |
1 | Acquisti et al. [35] | Experiment | Experiment on WTP/WTA money for private data and privacy |
5 | Bauer et al. [29] | Experiment | Survey-based experiment on value of Facebook user information from user perspective |
6 | Benndorf & Normann [31] | Experiment | Experiment to extraxt WTA money with take-it-or-leave-it offers |
10 | Danezis et al. [33] | Experiment | Experiment on WTA money for location tracking |
15 | Frik & Gaudeul [30] | Experiment | Method and experimental validation for elicitating the implicit value of privacy under risk |
17 | Grossklags & Acquisti [37] | Experiment | Experiment on WTP/WTA money for private data and privacy |
19 | Hann et al. [63] | Experiment | Conjoint analysis to estimate individual’s utility of mitigate privacy concerns |
26 | Lim et al. [64] | Experiment | Discrete choice experiment to estimate value of types of personal information leakage |
27 | Mahmoodi et al. [28] | Experiment | Experiment quantifying WTP for different levels of privacy on social media platforms & analysis of psychological factors (ongoing) |
31 | Nielsen [38] | Experiment | Experiment showing lay peoples reaction to data markets is diverse and shows unwillingness to participate in data market |
41 | Schomakers et al. [65] | Experiment | Mixed-method study on data sharing and privacy preferences in data markets |
47 | Spiekermann & Korunovska [3] | Experiment | Experiment on WTP/WTA money for private data and privacy |
48 | Staiano et al. [32] | Experiment | Living lab experiment focusing on pricing and correlated behaviour patterns |
52 | Winegar & Sunstein [36] | Experiment | Survey-based experiment on the disparity of WTP and WTA money to give up privacy |
2 | Acquisti et al. [7] | Literature Review | Literature review on privacy |
14 | Fricker & Maksimov [15] | Literature Review | Literature review on pricing of data products |
28 | Malgieri & Custers [14] | Literature Review | Literature review on pricing of personal data |
51 | Wdowin & Diepeveen[16] | Literature Review | Literature review on value of personal data |
9 | Coyle et al. [66] | Report | Policy Recommendation for capturing data value |
35 | OECD [5] | Report | Overarching report on pricing personal data |
40 | Rose et al. [67] | Report | Report on value of digital identity based on EU survey |
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Busch-Casler, J., Radic, M. (2022). Personal Data Markets: A Narrative Review on Influence Factors of the Price of Personal Data. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-05760-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05759-5
Online ISBN: 978-3-031-05760-1
eBook Packages: Computer ScienceComputer Science (R0)