P3S: Pertinent Privacy-Preserving Scheme for Remotely Sensed Environmental Data in Smart Cities

Sensing devices, high-performance networking, and privacy preservation algorithms have important roles to play in remotely sensed environmental data in smart cities. The data generated by these sensors are heterogeneous, vast, and sensitive. Therefore, it is imperative that adequate security mechanisms are put in place to protect environmental data from privacy breaches and malicious attacks, remotely sensed environmental data, such as weather conditions (windy, cloudy, or rainy), soil types, and other similar data, must be protected. The biggest risks of remotely connected devices are that sensitive information could be leaked and devices could be compromised. Considering these security threats, this article proposes a pertinent privacy-preserving scheme. The presented scheme is reliable for sensitive geosensed data in thwarting the aforementioned security issues. The data are concealed using two-factor authentication from the transmitter end. In this authentication, the signatures of device and receiver are overlapped for improved authentication. The failure in overlapping is identified by delayed signing time and noncoherent agreements. This identification is recurrently analyzed using federated learning. Therefore, the signing process is paused until the device verification is performed. Hence, if the device verification succeeds, then a new data privacy accumulation session is introduced. Contrarily, the accumulation is dropped, preventing compromised actual data from preserving accuracy. In two-factor authentication, lightweight digital signing cryptography is utilized. The proposed scheme maximizes the average authentication success rate and average overlapping factor by 8.86% and 12.20%, respectively. This scheme further reduces average authentication time, false data, and verification time by 10.14%, 9.70%, and 10.19%, respectively.

Automated summary

Sensing devices, high-performance networking, and privacy preservation algorithms are crucial for protecting remotely sensed environmental data in smart cities. This article proposes a privacy-preserving scheme that effectively safeguards sensitive geosensed data from security threats. The scheme utilizes two-factor authentication and federated learning to ensure data privacy and authentication. By employing lightweight digital signing cryptography, the proposed scheme achieves higher authentication success rate, improved overlapping factor, and reduced authentication time, false data, and verification time.