User-Friendly Privacy-Preserving Blockchain-based Data Trading

As the digital economy flourishes, the use of blockchain technology for data trading has seen a surge in popularity. Yet, previous approaches have frequently faltered in harmonizing security with user experience, culminating in suboptimal transactional efficiency. This study introduces a personalized local differential privacy framework, adeptly tackling data security concerns while accommodating the individual privacy preferences of data owners. Furthermore, the framework bolsters transaction flexibility and efficiency by catering to needs of data consumers for detailed queries and enabling data owners to effortlessly elevate their privacy budget to achieve greater financial returns. The efficacy of our approach is validated through a comprehensive series of experimental validations.