Range Proof-Based Noise Filtering Mechanism for loT Differential Privacy
Jianqi Wei, Yuling Chen, Yun Luo, Zai-Dong Li · 2024
With the rapid development of Internet of Things (loT) technology, the vast amount of data generated by its devices has raised widespread concern for user privacy pro-tection. Differential Privacy, as a stringent privacy protection measure, plays a significant role in safeguarding individual data. However, implementing Differential Privacy in the loT environment faces challenges in ensuring data utility. This paper focuses on proposing a Verifiable Differential Privacy (VDP) scheme based on zero-knowledge proofs, under the premise of ensuring data utility. The scheme requires data publishing entities to provide publicly verifiable proofs to confirm the reliability of the dataset and the effectiveness of privacy protection. By introducing a commitment mechanism and range proofs, our model not only protects the interests of data users but also enhances trust in the enforcement of privacy protection measures. The experimental results show that the scheme can effectively filter out unreasonable Differential Privacy noise, ensuring the privacy and reliability of data, offering a new approach to data privacy protection in the loT field.