A HOMOMORPHIC ENCRYPTION METHOD BASED ON CROWD NETWORKS

Abstract

To address the pressing security and privacy concerns in modern crowd sensing systems, this paper introduces a novel crowd sensing network approach based on the Multi-Functional Homomorphic Encryption (MFHE) architecture. the proposed method ensures end-to-end data confidentiality by eliminating the need for plaintext exposure throughout the entire sensing and transmission process—thus significantly enhancing security compared to conventional encryption techniques. The framework begins with the construction of a sensing model grounded in the classical crowd sensing architecture. To achieve secure, plaintext-free data communication, homomorphic encryption is applied. Built upon the MFHE architecture, the proposed encryption scheme incorporates advanced cryptographic techniques, including indistinguishability obfuscation and puncturable pseudorandom functions. These enhancements reduce ciphertext size and computational complexity, optimizing the system for practical deployment in crowd sensing environments. Experimental evaluations using real-world datasets confirm that the proposed solution not only provides robust privacy protection but also maintains high efficiency in terms of processing time.