Algorithm

Engineering

Materials science

Merkle tree

Computer security

Node (physics)

Theoretical computer science

Encryption

Cryptographic hash function

Operating system

Range (aeronautics)

Search engine

Range query (database)

Data mining

Computer science

Cloud computing

Outsourcing

Information privacy

Web search query

Database

Mathematics

Tree traversal

Political science

Query by Example

Mathematical analysis

Composite material

Tree (set theory)

Cryptography

Structural engineering

Information retrieval

Web query classification

Private information retrieval

IEEE Transactions on Knowledge and Data Engineering

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED <inline-formula><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> . By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> surpass the state-of-the-art methods in privacy-preservation level and efficiency.

Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data