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This study explores the factors impacting user adoption and trust in blockchain-based food delivery systems, with a spotlight on the Open Network for Digital Commerce (ONDC). In the evolving food delivery sector, blockchain offers transparency and efficiency. Through the Unified Theory of Acceptance and Use of Technology (UTAUT) lens, this research provides insights for businesses and policymakers, highlighting the importance of blockchain’s integration into food delivery.

The research employed the UTAUT and its extensions as the theoretical framework. A structured questionnaire was developed and disseminated to users of the ONDC platform, and responses were collected on a seven-point extended Likert scale. The analyses were undertaken employing the partial least squares (PLS) methodology and structural equation modelling (SEM).

Key factors like performance expectancy, effort expectancy and social influence were found influential for adoption. Trust played a central role, while perceived risk didn’t significantly mediate the adoption process. Digital culture didn’t significantly moderate the adoption intention.

This research adds to the existing body of knowledge by providing empirical insights into user adoption and trust in blockchain-based food delivery platforms. It is among the pioneer studies to apply the UTAUT model in the realm of blockchain-based food delivery platforms, thereby offering a unique perspective on the dynamics of user behaviour in this emerging field.

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.

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

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”.

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”.

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