VPSL: Verifiable privacy-preserving data search for cloud-assisted Internet of Things

• Main Authors: TONG, Qiuyun, MIAO, Yinbin, LIU, Ximeng, CHOO, Kim-Kwang Raymond, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Cloud computing; Cloud-assisted Internet of Things; Encryption; Indexes; Internet of Things; k-means clustering technique; Protocols; Result verification; Searchable symmetric encryption; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5999; https://ink.library.smu.edu.sg/context/sis_research/article/7002/viewcontent/VPSL_2020_av.pdf
• Institution: Singapore Management University

Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this paper we propose a Verifiable Privacy-preserving data Search scheme with Limited key-disclosure (VPSL) for cloud-assisted Internet of Things. VPSL first designs a trapdoor generation protocol for obtaining a trapdoor with disclosing limited key information and without revealing plaintext query points to others. Then, VPSL provides an efficient result verification and search processing by employing the Merkle hash tree structure and k-means clustering technique, respectively. VPSL is secure against the level-2 attack. Finally, an enhanced VPSL (called VPSL+) resisting the level-3 attack is constructed by introducing the random splitting technique. Empirical experiments demonstrate the accuracy and efficiency of VPSL or VPSL+ using real-world datasets.