Secure Location-Based Services (LBSs) in mobile cloud have gained widespread attention in the past decade. However, previous works mainly focus on spatial or spatial keyword query services and cannot support temporal filters simultaneously, which limits the service quality in practical applications. To address the above issue, we propose an efficient Keyword-associated Spatio-Temporal Structured Encryption (KSTSE) scheme that allows conjunctive queries according to spatio-temporal range and textual keywords on encrypted data in a mobile cloud. Specifically, we first transform geographic and temporal range queries into a unified encoding existence detection problem by combining S2 encoding and prefix encoding. Next, we build an efficient encrypted existence evaluation construction based on the circular shift and coalesce Bloom filter and symmetric hidden vector encryption. Finally, to support efficient queries on large-scale datasets, we design a hierarchical index tree structure, which can dynamically prune the search space according to the keyword and spatio-temporal range during the query process, reducing the query complexity toO(logN) Rigorous security analysis and performance evaluation show that the proposed KSTSE construction is adaptively secure under reasonable leakages and performs better than state-of-the-art schemes

With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and storage costs. To search through encrypted multimodal data in the cloud, privacy-preserving cross-modal retrieval (PPCMR) techniques have attracted extensive attention. However, most of the existing PPCMR schemes lack the ability to resist quantum attacks and have low search efficiency on large-scale datasets. To solve above problems, we first propose a basic PPCMR scheme FECMR using the enhanced Single-key Function-hiding Inner Product Functional Encryption for Binary strings (SFB-IPFE) and cross-modal hashing technology, which achieves the measurement of similarity over encrypted multimodal data while resisting quantum attacks. Then, we design an efficient index KM-tree utilizing the K-modes clustering algorithm. On this basis, we propose an improved scheme FECMR+, which achieves sub-linear search complexity. Finally, formal security analysis proves that our schemes are secure against quantum attacks, and extensive experiments prove that our schemes are efficient and feasible for practical application.

Privacy-preserving range query, which allows the server to implement secure and efficient range query on encrypted data, has been widely studied in recent years. Existing privacy-preserving range query schemes can realize effective range query, but usually suffer from the low efficiency and security. In order to solve the above issues, we propose an Efficient and Privacy-preserving encode-based Range Query over encrypted cloud data (namely basic EPRQ), which encodes the data and range by using Range Encode (REncoder), and then encrypts the codes via Additional Symmetric-Key Hidden Vector Encryption (ASHVE) technology. The basic EPRQ can achieve effective range query while ensuring privacy protection. Then, we split the codes to reduce the storage cost. We further propose an improved scheme, EPRQ+, which constructs a binary tree-based index to achieve faster-than-linear retrieval. Finally, our formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are feasible in practice, where EPRQ+ scheme improves the storage efficiency by about 4 times and the query efficiency by about 8 times compared to the basic EPRQ.

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basicPrivacy-preservingBloom filter-basedKeywordSearch scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a new index structure T-CSCBF utilizing theTwin Bloom Filter (TBF) technology. Based on this, we propose an improved scheme PBKS+, which assigns a unique inclusion identifier to each position in each BF with privacy protection. Formal security analysis proves that our schemes are secure against Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA), and extensive experiments using real-world datasets demonstrate that our schemes are feasible in practice.