SpringerLink
Log in

What is my privacy score? Measuring users’ privacy on social networking websites

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

Social networking websites usage is becoming popular these days among individuals and organizations. Several organizations and researchers started investigating how social networking websites can be used as a potential tool to innovate and improve the sales of products. However, in the hustle of using social networking sites, the users knowingly or unknowingly expose their personal data to unintended users. The literature identifies the need for privacy scores of a social networking website so that the users can easily identify the level of disclosure of their personal information on the website. Quantifying privacy on social networking websites is a new and trending area of research. We propose a novel approach to calculate the privacy score of a user on a social networking website. The calculated privacy score of the user takes into account the user’s personal profile attributes and settings along with the network characteristics of the social network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Number of social media users 2025 | Statista. (n.d.). Retrieved April 14, 2022, from https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/

  2. Aghasian, E., Garg, S., Gao, L., Yu, S., & Montgomery, J. (2017). Scoring users’ privacy disclosure across multiple online social networks. IEEE Access, 5, 13118–13130. https://doi.org/10.1109/ACCESS.2017.2720187

    Article  Google Scholar 

  3. Al-Asmari, H. A., & Saleh, M. S. (2019). A conceptual framework for measuring personal privacy risks in facebook online social network. In 2019 International Conference on Computer and Information Sciences, ICCIS 2019, 1–6. https://doi.org/10.1109/ICCISci.2019.8716477

  4. Backstrom, L., Boldi, P., Rosa, M., Ugander, J., & Vigna, S. (2012). Four degrees of separation. In Proceedings of the 4th Annual ACM Web Science Conference, 33–42.

  5. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks, 1999. Science, 286(5439), 509.

    Article  Google Scholar 

  6. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x

    Article  Google Scholar 

  7. Broido, A. D., & Clauset, A. (2019). Scale-free networks are rare. Nature. Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-08746-5

    Article  Google Scholar 

  8. Brown, G., Howe, T., Ihbe, M., Prakash, A., & Borders, K. (2008). Social networks and context-aware spam. In Proceedings of the 2008 ACM conference on Computer supported cooperative work, p. 403. https://doi.org/10.1145/1460563.1460628

  9. Casas, I., Hurtado, J., & Zhu, X. (2015). Social network privacy: Issues and Measurement. In Web Information Systems Engineering–WISE 2015: 16th International Conference, Miami, FL, USA, November 1–3, 2015, Proceedings, Part II 16 (pp. 488–502). Springer International Publishing. https://doi.org/10.1007/978-3-319-26187-4

  10. Chakraborty, R., Vishik, C., & Rao, H. R. (2013). Privacy preserving actions of older adults on social media: Exploring the behavior of opting out of information sharing. Decision Support Systems, 55(4), 948–956. https://doi.org/10.1016/j.dss.2013.01.004

    Article  Google Scholar 

  11. Chaudhury, A., Basuchowdhuri, P., & Majumder, S. (2012). Spread of information in a social network using influential nodes. In: Lecture notes in computer science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7301 LNAI(PART 2), 121–132

  12. Chen, D. B., Gao, H., Lü, L., & Zhou, T. (2013). Identifying influential nodes in large-scale directed networks: The role of clustering. PLoS ONE, 8(10), 1–10. https://doi.org/10.1371/journal.pone.0077455

    Article  Google Scholar 

  13. Choi, B. C. F., Jiang, Z. J., **ao, B., & Kim, S. S. (2015). Embarrassing exposures in online social networks: An integrated perspective of privacy invasion and relationship bonding. Information Systems Research, 26(4), 675–694. https://doi.org/10.1287/isre.2015.0602

    Article  Google Scholar 

  14. Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online privacy: Attitudes, behaviors, and unintended consequences. Journal of Computer-Mediated Communication, 15(1), 83–108. https://doi.org/10.1111/j.1083-6101.2009.01494.x

    Article  Google Scholar 

  15. Duan, Y., Ge, Y., & Feng, Y. (2020). Pricing and personal data collection strategies of online platforms in the face of privacy concerns. Electronic Commerce Research,. https://doi.org/10.1007/s10660-020-09439-8

    Article  Google Scholar 

  16. Fadigas, I. S., & Pereira, H. B. B. (2013). A network approach based on cliques. Physica A: Statistical Mechanics and Its Applications, 392(10), 2576–2587. https://doi.org/10.1016/j.physa.2013.01.055

    Article  Google Scholar 

  17. Feld, S. L., & Carter, W. C. (2002). Detecting measurement bias in respondent reports of personal networks. Social Networks, 24(4), 365-383. https://doi.org/10.1016/S0378-8733(02)00013-8

  18. Ghazinour, K., Matwin, S., & Sokolova, M. (2016). YOURPRIVACYPROTECTOR, A recommender system for privacy settings in social networks. Ar**v Preprint: ar**v:1602.01937.

  19. Ghoshal, G., & Barabási, A. L. (2011). Ranking stability and super-stable nodes in complex networks. Nature Communications, 2, 394. https://doi.org/10.1038/ncomms1396

    Article  Google Scholar 

  20. Gleiser, P. M., & Danon, L. (2003). Community structure in Jazz. Advances in Complex Systems, 06(04), 565–573. https://doi.org/10.1142/s0219525903001067

    Article  Google Scholar 

  21. Golbeck, J. (2013). Network structure and measures. Analyzing the Social Web, 5, 25–44. https://doi.org/10.1016/b978-0-12-405531-5.00003-1

    Article  Google Scholar 

  22. Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2(2), 129–233. https://doi.org/10.1561/2200000005

    Article  Google Scholar 

  23. Gross, R., Acquisti, A., & John, H. (2005). Information Revelation and Privacy in Online Social Networks (The Facebook case) Pre-proceedings version. ACM Workshop on Privacy in the Electronic Society (WPES), 2005. https://www.heinz.cmu.edu/~acquisti/papers/privacy-facebook-gross-acquisti.pdf

  24. Güney, E. (2019). An efficient linear programming based method for the influence maximization problem in social networks. Information Sciences, 503, 589–605. https://doi.org/10.1016/j.ins.2019.07.043

    Article  Google Scholar 

  25. Halim, F., Wu, Y., & Yap, R. H. C. (2008). Security issues in small world network routing. In Proceedings - 2nd IEEE international conference on self-adaptive and self-organizing systems, SASO 2008, 493–494. https://doi.org/10.1109/SASO.2008.21

  26. Hanneman, R. a, & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside. On-Line Textbook, 46(7), 5128–5130. https://doi.org/10.1016/j.socnet.2006.08.002

  27. Horawalavithana, S., Gandy, C., Flores, J. A., Skvoretz, J., & Iamnitchi, A. (2018). Diversity, topology, and the risk of node re-identification in labeled social graphs. Ar**v, 1–12.

  28. Jiang, Z., Heng, C. S., & Choi, B. C. F. (2013). Privacy concerns and privacy-protective behavior in synchronous online social interactions. Information Systems Research, 24(3), 579–595. https://doi.org/10.1287/isre.1120.0441

    Article  Google Scholar 

  29. Katzir, L., & Hardiman, S. J. (2015). Estimating clustering coefficients and size of social networks via random walk. ACM Transactions on the Web, 9(4), 539–549. https://doi.org/10.1145/2790304

    Article  Google Scholar 

  30. Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008). Tastes , ties, and time : A new social network dataset using Facebook.com. 30, 330–342. https://doi.org/10.1016/j.socnet.2008.07.002

  31. Liu, K., & Terzi, E. (2010). A framework for computing the privacy scores of users in online social networks. ACM Transactions on Knowledge Discovery from Data, 5(1), 1–30. https://doi.org/10.1145/1870096.1870102

    Article  Google Scholar 

  32. Liu, K. U. N. (2010). A framework for computing the privacy scores of users in online social networks. ACM Transactions on Knowledge Discovery from Data, 5(1), 1–30. https://doi.org/10.1145/1870096.1870102

    Article  Google Scholar 

  33. Liu, Y., Gummadi, K. P., Krishnamurthy, B., & Mislove, A. (2011). Analyzing Facebook privacy settings: User expectations vs. reality. In Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement, 61–70.

  34. Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007). ℓ-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data. https://doi.org/10.1145/1217299.1217302

    Article  Google Scholar 

  35. Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. In Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032

  36. Marsden, P. V. (2003). Interviewer effects in measuring network size using a single name generator. Social Networks, 25(1), 1–16. https://doi.org/10.1016/S0378-8733(02)00009-6

  37. Moustaka, V., Theodosiou, Z., Vakali, A., Kounoudes, A., & Anthopoulos, L. G. (2019). Εnhancing social networking in smart cities: Privacy and security borderlines. Technological Forecasting and Social Change, 142, 285–300. https://doi.org/10.1016/J.TECHFORE.2018.10.026

    Article  Google Scholar 

  38. Mousavi, R., Chen, R., Kim, D. J., & Chen, K. (2020). Effectiveness of privacy assurance mechanisms in users' privacy protection on social networking sites from the perspective of protection motivation theory. Decision Support Systems, 135, 113323. https://doi.org/10.1016/j.dss.2020.113323

  39. Nepali, R. K., & Wang, Y. (2013). 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops SONET : A SOcial NETwork Model for Privacy Monitoring and Ranking. 162–166. https://doi.org/10.1109/ICDCSW.2013.49

  40. Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics,. https://doi.org/10.1103/PhysRevE.74.036104

    Article  Google Scholar 

  41. Pensa, R. G., & Bioglio, L. (2017). Your Privacy, My Privacy ? On leakage risk assessment in online social networks. May 2018. https://doi.org/10.1007/978-3-319-71970-2

  42. Pensa, R. G., & Di Blasi, G. (2016a). A centrality-based measure of user privacy in online social networks. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, 1438–1439. https://doi.org/10.1109/ASONAM.2016.7752439

  43. Pensa, R. G., & Di Blasi, G. (2016b). A semi-supervised approach to measuring user privacy in online social networks. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9956 LNAI(October), 392–407. https://doi.org/10.1007/978-3-319-46307-0_25

  44. Pensa, R. G., & Di Blasi, G. (2017). A privacy self-assessment framework for online social networks. Expert Systems with Applications, 86, 18–31. https://doi.org/10.1016/j.eswa.2017.05.054

    Article  Google Scholar 

  45. Pensa, R. G., Di Blasi, G., & Bioglio, L. (2019). Network-aware privacy risk estimation in online social networks. Social Network Analysis and Mining, 9(1), 1–15. https://doi.org/10.1007/s13278-019-0558-x

    Article  Google Scholar 

  46. Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2020). The risk of racial bias in hate speech detection. In ACL 2019 - 57th Annual meeting of the association for computational linguistics, proceedings of the conference, pp. 1668–1678. https://doi.org/10.18653/v1/p19-1163

  47. Saxena, A., Gera, R., & Iyengar, S. R. S. (2017). A faster method to estimate closeness centrality ranking. Ar**v, pp. 1–25.

  48. SNAP: Network datasets: Social circles. (n.d.). Retrieved September 7, 2020, from https://snap.stanford.edu/data/ego-Facebook.html

  49. Spiekermann, S., & Korunovska, J. (2017). Towards a value theory for personal data. Journal of Information Technology, 32(1), 62–84. https://doi.org/10.1057/jit.2016.4

    Article  Google Scholar 

  50. Srivastava, A., & Geethakumari, G. (2013). Measuring privacy leaks in Online Social Networks. Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, 2095–2100. https://doi.org/10.1109/ICACCI.2013.6637504

  51. Sweeney, L. (2002). k-Anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 10(5), 1–14.

    Google Scholar 

  52. Talukder, N., Ouzzani, M., Elmagarmid, A. K., Elmeleegy, H., & Yakout, M. (2010, March). Privometer: Privacy protection in social networks. In 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010) (pp. 266–269). IEEE. https://doi.org/10.1109/ICDEW.2010.5452715

  53. Tian, H., Lu, Y., Liu, J., & Yu, J. (2018). Betweenness centrality based k-anonymity for privacy preserving in social networks. ACM International Conference Proceeding Series. https://doi.org/10.1145/3282353.3282366

    Article  Google Scholar 

  54. Traud, A. L., Mucha, P. J., & Porter, M. A. (2012). Social structure of Facebook networks. Physica A: Statistical Mechanics and Its Applications, 391(16), 4165–4180. https://doi.org/10.1016/j.physa.2011.12.021

    Article  Google Scholar 

  55. Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The Anatomy of the Facebook Social Graph. 1–17. http://arxiv.org/abs/1111.4503

  56. Walsh, D., Parisi, J. M., & Passerini, K. (2017). Privacy as a right or as a commodity in the online world: The limits of regulatory reform and self-regulation. Electronic Commerce Research, 17(2), 185–203. https://doi.org/10.1007/s10660-015-9187-2

    Article  Google Scholar 

  57. Wang, Y., Nepali, R. K., & Nikolai, J. (2014). Social network privacy measurement and simulation. In 2014 International Conference on Computing, Networking and Communications, ICNC 2014, December 2015, 802–806. https://doi.org/10.1109/ICCNC.2014.6785440

  58. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918

    Article  Google Scholar 

  59. Wilson, C., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2012). Beyond social graphs: User interactions in online Social networks and their implications. ACM Transactions on the Web. https://doi.org/10.1145/2382616.2382620

    Article  Google Scholar 

  60. Yang, Y., Wang, X., Chen, Y., Hu, M., & Ruan, C. (2020). A novel centrality of influential nodes identification in complex networks. IEEE Access, 8, 58742–58751. https://doi.org/10.1109/ACCESS.2020.2983053

    Article  Google Scholar 

  61. Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4), 452–473. https://doi.org/10.1086/jar.33.4.3629752

    Article  Google Scholar 

  62. Zhang, J., Li, H., (Robert) Luo, X., & Warkentin, M. (2017). Exploring the effects of the privacy-handling management styles of social networking sites on user satisfaction: A conflict management perspective. Decision Sciences, 48(5), 956–989. https://doi.org/10.1111/deci.12243

    Article  Google Scholar 

  63. Zhang, Y., Bao, Y., Zhao, S., Chen, J., & Tang, J. (2016). Identifying Node Importance by Combining Betweenness Centrality and Katz Centrality. In Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015, 354–357. https://doi.org/10.1109/CCBD.2015.19

  64. Zheleva, E., & Getoor, L. (2011). Social network data analytics. Berlin: Springer. https://doi.org/10.1007/978-1-4419-8462-3

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Xavier Institute of Management, XIM University, Bhubaneswar, India

    Amit Kumar Srivastava

  2. Indian Institute of Management Indore, Indore, India

    Rajhans Mishra

Authors
  1. Amit Kumar Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajhans Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajhans Mishra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, A.K., Mishra, R. What is my privacy score? Measuring users’ privacy on social networking websites. Electron Commer Res (2024). https://doi.org/10.1007/s10660-023-09796-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10660-023-09796-0

Keywords

Search

Navigation