Rphx: Result Pattern Hiding Conjunctive Query Over Private Compressed Index Using Intel SGX
Qin Jiang, Ee‐Chien Chang, Yong Qi, Saiyu Qi, Pengfei Wu, Jianfeng Wang · IEEE Transactions on Information Forensics and Security · 2022
Deploying data storage and query service in an untrusted cloud server raises critical privacy and security concerns. This paper focuses on the fundamental problem of processing conjunctive keyword queries over an untrusted cloud in a privacy-preserving manner. Previous tree-based searchable symmetric encryption (SSE) schemes, such asIBTreeandVBTree, can process conjunctive keyword queries in a secure and efficient way. However, these schemes cannot address “Result Pattern (RP)” leakage, which can be used to recover the keywords contained in a conjunctive keyword query. To combat this challenging problem, we propose a result pattern hiding conjunctive query scheme namedRphxusing Intel SGX. In particular, we first propose a new “SGX-aware” compressed index namedVIBTby combining variable-length bloom filter tree, matryoshka filter and online cipher. To achieveRPhiding, we then introduce a new tree-based SSE scheme namedRphxby deployingVIBTto Intel SGX. Security analysis shows thatRphxcan enhance the security requirements by hidingRPleakage under the IND-CKA2 security model. Experimental results show thatVIBTgains at least$30\times $improvement in storage efficiency andRphxcan achieve comparable search efficiency comparing with previous works.