Fair and dynamic data sharing framework in cloud-assisted Internet of Everything

• Main Authors: MIAO, Yinbin, LIU, Ximeng, CHOO, Kim-Kwang Raymond, DENG, Robert H., WU, Hongjun, LI, Hongwei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Fair arbitration; full-update operation; Internet of Everything (IoE); Internet of Things (IoT); multiowner setting; searchable encryption (SE); Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/4676; https://ink.library.smu.edu.sg/context/sis_research/article/5679/viewcontent/Fair_Dynamic_Data_Sharing_CA_IoE_av.pdf
• Institution: Singapore Management University

Cloud-assisted Internet of Things (IoT) is increasingly prevalent in our society, for example in home and office environment; hence, it is also known as cloud-assisted Internet of Everything (IoE). While in such a setup, data can be easily shared and disseminated (e.g., between a device, such as Amazon Echo and the cloud, such as Amazon AWS), there are potential security considerations that need to be addressed. Thus, a number of security solutions have been proposed. For example, searchable encryption (SE) has been extensively studied due to its capability to facilitate searching of encrypted data. However, threat models in most existing SE solutions rarely consider the malicious data owner and semi-trusted cloud server at the same time, particularly in dynamic applications. In a real-world deployment, disputes between above two parties may arise as either party will accuse the other of some misbehavior. Furthermore, efficient full-update operations (e.g., data modification, data insertion, and data deletion) are not typically supported in the cloud-assisted IoE deployment. Therefore, in this paper, we present a fair and dynamic data sharing framework (FairDynDSF) in the multiowner setting. Using FairDynDSF, one can check the correctness of search results, achieve fair arbitration, multikeyword search, and dynamic update. We also prove that FairDynDSF is secure against inside keyword guessing attack and demonstrate its efficiency by evaluating its performance using various datasets.