Secure and Privacy-Preserved Data Collection for IoT Wireless Sensors

Abstract

The captured data from smart devices via Internet of Things (IoT) wireless sensors are vulnerable to numerous online and offline attacks and unauthorized accesses, hence, some digital signature and encryption solutions have been designed to ensure public verifiability, data integrity, and confidentiality. However, there are still some issues to be addressed. For example, the data source is revealed to the public due to the public verifiability of digital signatures, in which authentication is transferrable. Moreover, computation of these data can only be done after decryption, restricting outsourced computation, such as a computing facility from a cloud. The best approach of private computation, which supports outsourced computation, is based on homomorphic encryption. However, significant computational overhead is a concern. To deal with these issues, in this article, we propose an efficient and provably secure scheme based on designated-verifier proofs, deniable authentication and homomorphic encryption for secure and lightweight data collection, batch verification, and data analysis in the privacy-preserved IoT wireless sensors applications. The main contribution of our work is the privacy-preserved IoT wireless sensors system along with a novel deniable authenticated homomorphic encryption scheme that can securely aggregate data from IoT wireless sensors for secure outsourced applications. To prove the security and efficiency of our proposed scheme, we provide formal security analysis and performance comparisons for IoT wireless sensors.