In a breakthrough that aims to address growing privacy concerns surrounding smart infrastructure, a team of researchers has unveiled a novel approach to improve the quality of mobile crowdsensing data. By integrating dual blockchains into the latest model, the researchers anticipate not only bolstering the security of citizens’ personal information but also weeding out untrustworthy users during the mobile sensing data collection process.
Lee Soojin, a researcher in the Department of Electronic and Electrical Engineering at Hanyang University, showcased her team’s groundbreaking work at the 24th World Conference on Information Security Applications (WISA), held on August 23 in South Korea. Collaborating with Lee on this research were Zhang Yan, Bai Yuhao, Li Ming, and Seo Seung-Hyun.
In their research paper titled “A Blockchain-based Mobile Crowdsensing and Its Incentive Mechanism,” the team from the Embedded Security & Privacy Lab at Hanyang University introduced a novel hybrid approach. By melding a public blockchain with a consortium blockchain, they aim to strike a balance between robust security and optimized performance.
According to the study, the blockchain-based mobile crowdsensing system model (BCMCS) tackles several key challenges plaguing today’s mobile crowdsensing landscape. By selectively adopting strengths from each blockchain model, the system aims to eliminate performance bottlenecks and patch up privacy vulnerabilities.
While public blockchains are open to anyone wishing to participate in their internal activities, consortium blockchains restrict access to a predetermined group. This exclusivity often results in consortium blockchains being viewed as more secure, even if they sacrifice some of the transparency found in public blockchains.
Moreover, the BCMCS model offers an added perk: it encourages active participation among verified users through a quality-driven incentive mechanism. “By capitalizing on the consortium chain’s strengths—transaction efficiency, enhanced privacy, and robust security—computationally demanding tasks in the sensing process can be executed more efficiently,” the researchers noted.
Lee also underscored the critical role of high-quality data. “In the context of smart cities, collecting quality data is paramount,” the researcher stated in her presentation. To assess the caliber of the data gathered through mobile crowdsensing, which refers to a technique used for data collection in smart city operations via mobile sensing devices, Lee and her team have implemented “an advanced Sybil-resistant account grouping method, bolstered by an enhanced truth discovery algorithm for grouping.”
The term “Sybil” refers to an attack in which an actor creates multiple deceptive identities on a blockchain for nefarious purposes. “Truth discovery,” on the other hand, is the process of pinpointing the most accurate data value when confronted with conflicting information from various sources. In layman’s terms, the researchers are striving to weed out fraudulent mobile users from the smart infrastructure. They’re employing a series of verification techniques designed to boost network trust, while also incentivizing the active participation of legitimate users—a crucial component for the system’s success.
“By appropriately assigning responsibilities to the public and consortium chains, a balance is achieved between system security and performance,” added the researchers.
