Smart Contract Coordinated Privacy Preserving Crowd-Sensing Campaigns

TL;DR

The paper addresses privacy and data availability challenges in crowd-sensing by proposing a blockchain-based system in which a smart contract coordinates data collection, encryption, and decentralized storage to form a data marketplace. Anonymity is reinforced through data chunking and participation thresholds, while decentralization reduces single-point-of-failure risks. An incentive mechanism rewards participants and deters cheating, with rewards adapting to data scarcity and participation levels. A Python-based simulator demonstrates that participation improves data credibility, and data from sparsely populated areas commands higher rewards, promoting geographic diversity and reducing manipulation.

Abstract

Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. However, data availability and privacy concerns pose significant challenges. Traditional methods like data encryption and anonymization, while essential, may not fully address these issues. For instance, in sparsely populated areas, anonymized data can still be traced back to individual users. Additionally, the volume of data generated by users can reveal their identities. To develop credible crowd-sensing systems, data must be anonymized, aggregated and separated into uniformly sized chunks. Furthermore, decentralizing the data management process, rather than relying on a single server, can enhance security and trust. This paper proposes a system utilizing smart contracts and blockchain technologies to manage crowd-sensing campaigns. The smart contract handles user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive policies within the smart contract encourage user participation and data diversity. Simulation results confirm the system's viability, highlighting the importance of user participation for data credibility and the impact of geographical data scarcity on rewards. This approach aims to balance data origin and reduce cheating risks.

Figures (7)

• Figure 1: Scenario of our study. Users in an area may report data to the campaign, only if a minimum population is available due to privacy concerns.
• Figure 2: System protocol
• Figure 3: Accumulated reward ECDF varying the minimum number of persons for each SC.
• Figure 4: Accumulated reward versus the population density. A higher number of people translates into a higher data offer, hence reduced rewards.
• Figure 5: Satisfied ratio versus data freshness. Decreasing the requirements for data freshness allows SC to be satisfied with a lower population density.