Secure and efficient fine-grained multiple file sharing in cloud-assisted crowd sensing networks

Abstract

Mobile crowd sensing significantly facilitates a broad range of emerging applications by treating mobile devices of ordinary users as basic sensing units to distribute sensing tasks and collect sensing data. Owing to the continuity of data sensing and richness of content, resource-constrained mobile devices always outsource their collected data into the cloud for file sharing. The existing work enables only batch decryption limited to RSA algorithm and merely allows one specific file to be shared among multiple authorized receivers by exploiting the technique of attribute-based encryption (ABE). In this paper, a generalized efficient batch cryptosystem GBC and its extension GBCS are firstly proposed to achieve both batch encryption and batch decryption from any public key encryption algorithm. Then, our proposed GBC is further extended to FMFS, TMFS and MMFS to address secure multiple file sharing in the cloud-assisted mobile crowd sensing network, respectively for fine-grained multi-receiver multi-file sharing, file authority transfer and multiple file owners’s settings. Finally, formal security proof is given to show our proposed generalized efficient batch cryptosystems GBC, GBCS and secure multiple file sharing schemes are respectively secure against adaptive chosen ciphertext attack (CCA2) in the random oracle model and under the assumption on the computational hardness of the subset decision problem in the standard model. Extensive evaluations illustrate our secure multiple file sharing in cloud-assisted crowd sensing network based on the newly-devised GBC dramatically outperforms the state-of-the-art in storage, computational and communication overhead.