Abstract
Social media plays a vital role in the user community all over the globe. It makes it easy to communicate from one person to another person through these social media platforms. But these platforms are coming under various security issues and privacy of user-related data that make it tough to maintain. Test data generated from existing tools are used for analysis on these platforms. According to various roles in any real-world application, environment for the user, such as community deduction, analyzing user profiles, and preventing security threats, is performed on these data. In this paper, we have surveyed various factors related to security and privacy in a social network and listed various advantages and disadvantages of various approaches, thereby it acts as a base paper for future research work in the field of social networking.
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
Similar content being viewed by others
References
Kumar, C., et al.: Online social network security: a comparative review using machine learning and deep learning. Neural Process. Lett. (2021)
Bashit, A., et al.: A comprehensive survey of AI-enabled phishing attacks and deduction techniques. Telecommun. Syst. (2021)
Charu Singh, M.: Phishing website deduction based on machine learning: a survey. In: International Conference on Advanced Computing & Communication Systems (ICSCCS) (2020)
Alabdan, R.: Phishing attacks survey: types, vectors and technical approaches. Future Internet (2020)
Lim, W.H., et al.: Phishing security: attack, deduction and prevention mechanisms. In: International Conference on Digital Transformations and Applications (ICDXA) (2020)
Hou, Q., et al.: Survey on data analysis in social media: a practical application aspect. Big Data Min. Anal. (3) (2020)
Zhang, Z., et al.: A Crowdsourcing method for online social networks security assessment based on human-centric computing. Hum. Centric Comput. Springer open access journal (2020)
Islam, M.R., et al.: Deep Learning for misinformation deduction on social networks: a survey and new perspectives. Soc. Netw. Anal. Min. Springer (2020)
Isaak, J., et al.: User data privacy: Facebook, Cambridge analytica and privacy protection, policy corner. Published by IEEE Society (2018)
Personal Privacy, Awareness and Control as per IEEE—USA. https://globalpolicy.ieee.org/
Ali, S., et al.: Privacy and security issues in online social networks. Future Internet 2018, MDPI (2018)
Abbasi, A., et al.: The phishing funnel model: a design articraft to predict user susceptibility to phishing websites. Inf. Syst. Res. Published by Institute for Operations Research and the Management Sciences (INFORMS), Feb (2021)
Hafiene, N., et al.: Influential node deduction in Dynamic Social networks: A Survey. Elsevier, Expert System and Applications (2020)
Aghasian, E., et al.: An automated model to score the privacy of unstructured information—social media case. Comput. Secur. (2020)
Alshaikh, M., et al.: Social network analysis and mining: privacy and security on twitter. IEEE Transp. Electrification Conf Expo (ITEC) (2020)
Sharma, V.P., et al.: An analysis of current research trends and applications in natural language processing. Int. J. Adv. Innov. Res. 7(1) (2020)
De Olivera, N.R., et al.: Identifying fake news on social networks based on natural language processing and trends. Information, MDPI (2021)
Tiberu-marian, G.: Natural language processing model for automatic analysis of cyber-security related documents. Symmetry, DPI (2020)
Kou, H., et al.: Building trust/distrust relationships on signed social service network through privacy-aware link prediction process. Appl. Soft Comput. J. (2020). https://doi.org/10.1016/j.asoc.2020.106942
Schwartz-Chassidim, H., et al.: Selectivity in posting on social networks: the role of privacy concerns, social capital, and technical literacy. Elsevier—Helion Jan (2020)
Nicolas, C., Kim, J., Chi, S.: Natural language processing-based characterization of top-down communication in smart cities for enhancing citizen alignment. Sustain. Cities Soc. (2020).https://doi.org/10.1016/j.scs.2020.102674
Akar, E., Mardikyan, S.: User roles and contribution patterns in online communities: a managerial perspective. Sage Open-Research Paper, Page 1–19, July (2018). https://doi.org/10.1177/2158244018794773
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jayaram, B., Jayakumar, C. (2022). A Survey on Security and Privacy in Social Networks. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore. https://doi.org/10.1007/978-981-16-9573-5_58
Download citation
DOI: https://doi.org/10.1007/978-981-16-9573-5_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9572-8
Online ISBN: 978-981-16-9573-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)