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A machine learning framework for security and privacy issues in building trust for social networking

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Abstract

Technological advancement has led to a dynamic change in how people interact. The interaction can be classified as—social networks (virtual) and real-life (physical) interaction. The relationships among nodes of social networks depend on communication among nodes and have been established by transmitting messages publicly or privately, depending on the trust among nodes. However, it poses three main challenges: (i) nodes can reveal their private message, (ii) private conversation among nodes can be hacked, and (iii) stored messages can be sent to another node, therefore, compromising privacy. The paper presents a privacy-preserving model using two major techniques, Binomial Distribution and Fuzzy Logic. This process has been followed by building predictive models using machine learning to provide potential directions to mitigate security threats posed to social networking platforms. The paper also provides a wholesome discussion of model design for predictive modelling, whereas most state-of-the-art techniques were primarily concerned about delivering potential directions for predictive modelling. The employed statistical and predictive modelling techniques suggest that friendship relationships in real life among people would transcend to ensure a secure and privacy-preserving relationship among them on social networks. Empirical results indicated that XG Boost supersedes the state-of-the-art models by achieving a predictive accuracy of 92.035%. In contrast, introducing new statistical features improves the performance of all our predictive models.

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Data availability

The datasets generated during and/or analysed during the current study are available in the “Who is a Friend?” at Kaggle.com, Available at: https://www.kaggle.com/c/whoisafriend/data. This is also mentioned under Reference Section at Ref. [23].

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Funding

The work experimented in this paper did not receive any funds or financial support from any agency or institution.

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Authors and Affiliations

  1. Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh, India

    Robin Singh Bhadoria

  2. Bhimtal, India

    Naman Bhoj

  3. School of Computer Science, Swami Vivekananda University, Barrackpore, West Bengal, India

    Manoj Kumar Srivastav

  4. Department of Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology Jais, Amethi, 229304, Uttar Pradesh, India

    Rahul Kumar

  5. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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  1. Robin Singh Bhadoria
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  2. Naman Bhoj
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  4. Rahul Kumar
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  5. Balasubramanian Raman
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Correspondence to Rahul Kumar.

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Bhadoria, R.S., Bhoj, N., Srivastav, M.K. et al. A machine learning framework for security and privacy issues in building trust for social networking. Cluster Comput 26, 3907–3930 (2023). https://doi.org/10.1007/s10586-022-03787-w

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  • DOI: https://doi.org/10.1007/s10586-022-03787-w

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