How to dissolve the “privacy paradox” in social networks? A game approach based on privacy calculus

Abstract

Purpose

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

Design/methodology/approach

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

Findings

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

Practical implications

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

Originality/value

These findings offer some insights into users’ privacy protection and personal data sharing.

Citation

Zhang, X., Cai, Y., Liu, F. and Zhou, F. (2024), "How to dissolve the “privacy paradox” in social networks? A game approach based on privacy calculus", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-03-2024-0544