# A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
## 8 Conclusions and Future Works  
This paper introduces a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. The model safeguards the privacy of local data by designing an adaptive local differential privacy algorithm, MDLDP, and ensures fair data transactions using verifiable encrypted signatures. Instead of the traditional single arbitrator found in conventional verifiable encrypted signatures, the proposed model introduces a committee. Through threshold signature technology, the arbitration private key is divided and managed by the committee members. In the event of transaction disputes, any \(t\) members of the committee can collaborate to reconstruct the arbitration private key for arbitration. Theoretical analysis shows that our method can effectively protect the privacy of data providers, ensure fair transaction markets, and safeguard the security of arbitration private keys, preventing the theft or loss of arbitration private keys due to single-point failures. In addition, this method also prevents collusion between dishonest traders and arbitrators and achieves fair payment in transactions. We evaluated the MDLDP algorithm on a real-world dataset. Compared with existing methods, although the MDLDP algorithm has slightly more time consumption than the method with the lowest time consumption, it has better privacy protection and can more accurately protect users' privacy datasets. Meanwhile, this method will not reduce data availability. Due to the fact that time consumption does not significantly change with changes in privacy budget or data volume, the limited excess time consumption of the MDLDP method is acceptable and has good practicality.  
However, although we conducted simulation experiments using MATLAB, there is a lack of specific implementation details on real blockchain platforms, such as node registration and approval, consensus algorithms, transactions, and data storage. We plan to further validate the model on specific blockchain platforms such as Hyperledger in the future. The model proposed in this article assumes that honest data providers provide effective and high-quality data. How to verify data quality and ensure data validity is a problem that needs to be solved in the future. In addition, it is also a valuable study that high availability and low latency access may be required for IoT systems.  
Conceptualization, W.Z. ([PERSON]) and D.Z.; methodology, D.Z.; software, D.Z. and G.H.; validation, W.Z. ([PERSON]), D.Z. and W.Z. ([PERSON]); formal analysis, D.Z.; investigation, X.W. and W.Z. ([PERSON]); resources, X.W.; data curation, D.Z. and W.Z. ([PERSON]); writing--original draft preparation, D.Z.; writing--review and editing, W.Z. ([PERSON]) and G.H.; visualization, X.W.; supervision, W.Z. ([PERSON]); project administration, W.Z. ([PERSON]); funding acquisition, W.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 4: Time consumption for perturbing different data sizes.  
**Funding:** This work is supported by the National Natural Science Foundation of China under Grant Numbers 61502262 and 62001262.  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: ([[https://archive.ics.uci.edu/dataset/360/air+quality](https://archive.ics.uci.edu/dataset/360/air+quality)]([https://archive.ics.uci.edu/dataset/360/air+quality](https://archive.ics.uci.edu/dataset/360/air+quality)), accessed on 31 July 2023).  
**Conflicts of Interest**: Authors [PERSON] and [PERSON] were employed by the company Qingdao Haier Smart Technology R&D Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.