Skip to main content

Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks

  • Conference paper
  • First Online:
Emerging Technologies in Data Mining and Information Security

Abstract

Twenty-first century, the era of Internet, social networking platforms like Facebook and Twitter play a predominant role in everybody’s life. Ever increasing adoption of gadgets such as mobile phones and tablets have made social media available all times. This recent surge in online interaction has made it imperative to have ample protection against privacy breaches to ensure a fine grained and a personalized data publishing online. Privacy concerns over communal data shared amongst multiple users are not properly addressed in most of the social media. The proposed work deals with effectively suggesting whether or not to grant access to the data which is co-owned by multiple users. Conflicts in such scenario are resolved by taking into consideration the privacy risk and confidentiality loss observed if the data is shared. For secure sharing of data, a trust framework based on the user’s interest and interaction parameters is put forth. The proposed work can be extended to any data sharing multiuser platform.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Facebook–cambridge analytical data scandal (2018). https://en.wikipedia.org/wiki/Facebook%E2%80%93CambridgeAnalyticadatascandal

  2. Meet gnip, the company that’s using twitter’s data to disrupt the online advertising industry (2018). https://www.businessinsider.com/gnip-2011-8?IR=T

  3. Ghafari SM, Beheshti A, Joshi A, Paris C, Mahmood A, Yakhchi S, Orgun MA (2020) A survey on trust prediction in online social networks. IEEE Access 8:144292–144309

    Google Scholar 

  4. Liu S, Zhang L, Yan Z (2018) Predict pairwise trust based on machine learning in online social networks: a survey. IEEE Access 6:51297–51318

    Google Scholar 

  5. Hu H, Ahn GJ (2011) Multiparty authorization framework for data sharing in online social networks. In: Li Y (ed) Data and applications security and privacy XXV. Springer Berlin Heidelberg, pp 29–43

    Google Scholar 

  6. Wang J, Jing X, Yan Z, Fu Y, Pedrycz W, Yang LT (2020) A survey on trust evaluation based on machine learning. ACM Comput Surv 53(5). https://doi.org/10.1145/3408292

  7. Lim KH, Datta A (2012) Finding twitter communities with common interests using following links of celebrities. In: Proceedings of the 3rd international workshop on modeling social media, ser. MSM’12. Association for Computing Machinery, New York, NY, USA, pp 25–32. https://doi.org/10.1145/2310057.2310064

  8. Nguyen TH, Tran DQ, Dam GM, Nguyen MH (2018) Estimating the similarity of social network users based on behaviors. Vietnam J Comp Sci 5(2):165–175

    Article  Google Scholar 

  9. Yang C, Zhou Y, Chiu DM (2016) Who are like-minded: Mining user interest similarity in online social networks

    Google Scholar 

  10. Ma H (2014) On measuring social friend interest similarities in recommender systems. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ser. SIGIR’14. Association for Computing Machinery, New York, NY, USA, pp 465–474. https://doi.org/10.1145/2600428.2609635

  11. Zhou K, Martin A, Pan Q (2015) A similarity-based community detection method with multiple prototype representation. Phys A: Stat Mech Appl 438:519–531. https://doi.org/10.1016/j.physa.2015.07.016

  12. Schwartz-Chassidim H, Ayalon O, Mendel T, Hirschprung R, Toch E (2020) Selectivity in posting on social networks: the role of privacy concerns, social capital, and technical literacy. Heliyon 6(2):e03298. http://www.sciencedirect.com/science/article/pii/S2405844020301432

  13. Almuzaini F, Alromaih S, Althnian A, Kurdi H (2020) Whatstrust: a trust management system for whatsapp. Electronics 9(12). https://www.mdpi.com/2079-9292/9/12/2190

  14. Zhang Z, Jing J, Wang X, Choo KR, Gupta BB (2020) A crowdsourcing method for online social networks security assessment based on human-centric computing. Hum centric Comput Inf Sci 10:23. https://doi.org/10.1186/s13673-020-00230-0

  15. Baek S, Kim S (2014) Trust-based access control model from sociological approach in dynamic online social network environment. Scien World J 2014

    Google Scholar 

  16. Yassein MB, Aljawarneh S, Wahsheh Y (2019) Hybrid real-time protection system for online social networks. In: Foundations of science, pp 1–30

    Google Scholar 

  17. Zolfaghar K, Aghaie A (2010) Mining trust and distrust relationships in social web applications. In: Proceedings of the 2010 IEEE 6th international conference on intelligent computer communication and processing, pp 73–80

    Google Scholar 

  18. Saeidi S (2020) A new model for calculating the maximum trust in online social networks and solving by artificial bee colony algorithm. Comput Social Netw 7:1–21

    Article  Google Scholar 

  19. Son J, Choi W, Choi SM (2020) Trust information network in social internet of things using trust-aware recommender systems. Int J Distrib Sens Netw 16(4):1550147720908773. https://doi.org/10.1177/1550147720908773

  20. Ding K, Zhang J (2020) Multi-party privacy conflict management in online social networks: a network game perspective. IEEE/ACM Trans Netw 28(6):2685–2698

    Article  Google Scholar 

  21. Hu H, Ahn G, Jorgensen J (2013) Multiparty access control for online social networks: model and mechanisms. IEEE Trans Knowl Data Eng 25(7):1614–1627

    Article  Google Scholar 

  22. Ali S, Rauf A, Islam N, Farman H (2017) A framework for secure and privacy protected collaborative contents sharing using public osn. Clust Comput 22:7275–7286

    Article  Google Scholar 

  23. Ilia P, Carminati B, Ferrari E, Fragopoulou P, Ioannidis S (2017) Sampac: socially-aware collaborative multi-party access control. In: Proceedings of the seventh ACM on conference on data and application security and privacy, ser. CODASPY’17. Association for Computing Machinery, New York, NY, USA, pp 71–82. https://doi.org/10.1145/3029806.3029834

  24. Vishwamitra N, Li Y, Wang K, Hu H, Caine K, Ahn GJ (2017) Towards pii-based multiparty access control for photo sharing in online social networks. In: Proceedings of the 22nd ACM on symposium on access control models and technologies, ser. SACMAT’17 Abstracts. Association for Computing Machinery, New York, NY, USA, pp 155–166. https://doi.org/10.1145/3078861.3078875

  25. Khan J, Lee S (2018) Online social networks (osn) evolution model based on homophily and preferential attachment. Symmetry 10(11). https://www.mdpi.com/2073-8994/10/11/654

  26. Michelson M, Macskassy SA (2010) Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on analytics for noisy unstructured text data, ser. AND’10. Association for Computing Machinery, New York, NY, USA, pp 73–80. https://doi.org/10.1145/1871840.1871852

  27. Krithika LB, Roy P, Jerlin MA (2017) Finding user personal interests by tweet-mining using advanced machine learning algorithm in r. IOP Conf Ser: Mater Sci Eng 263:042071, nov 2017. https://doi.org/10.1088/1757-899x/263/4/042071

  28. Khan J, Lee S (2019) Implicit user trust modeling based on user attributes and behavior in online social networks. IEEE Access 7:142826–142842

    Google Scholar 

  29. Hu H, Ahn GJ, Jorgensen J (2011) Detecting and resolving privacy conflicts for collaborative data sharing in online social networks. In: Proceedings of the 27th annual computer security applications conference, ser. ACSAC’11. Association for Computing Machinery, New York, NY, USA, pp 103–112. https://doi.org/10.1145/2076732.2076747

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Nisha P. Shetty or Balachandra Muniyal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shetty, N.P. et al. (2023). Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Shahnaz, C. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_5

Download citation

Publish with us

Policies and ethics