With the widespread adoption of cloud storage, time-series databases have become indispensable for managing and analyzing sequential data generated on the user side over time (i.e., time-series data), thereby alleviating the computational and storage burden on resource-constrained users. However, critical security and privacy challenges-such as query privacy leakage, data exposure, and threats to storage integrity-remain inadequately addressed by existing solutions. To this end, we propose VMPQ, an efficient protocol for privacy-preserving and verifiable multi-predicate queries over time-series databases. Specifically, we introduce a new cryptographic primitive, verifiable offline/online private information retrieval (V-OO-PIR), which supports sublinear retrieval complexity while simultaneously ensuring both query privacy and result verifiability against untrusted servers. Building on V-OO-PIR, we design a dual-layer security framework that integrates replicated secret sharing (RSS) and secure multiparty computation (MPC): (1) RSS splits time-series data into two shares stored across two non-colluding servers, ensuring data confidentiality and mitigating exposure risks, and (2) MPC performs secure multiplication directly on these shares, enabling efficient evaluation of multi-predicate queries without reconstructing the original data. As a result, VMPQ ensures query privacy by preventing servers from inferring user interests across multiple predicates, while simultaneously guaranteeing data confidentiality and the verifiability of query results. Theoretical analysis confirms the security of VMPQ against malicious adversaries. Experimental results demonstrate that VMPQ reduces query latency by up to 5× compared to the state-of-the-art solution Waldo, while also enhancing throughput and preserving high storage efficiency through optimized database encoding.

Secure join schemes, an important class of queries over encrypted databases, have attracted increasing attention. While efficient querying is paramount, data owners also emphasize the significance of privacy preservation. The state-of-the-art JXT (Jutla and Patranabis ASIACRYPT 2022) enables efficient join queries over encrypted tables with a symmetric-key solution. However, we observe that JXT inadvertently leaks undesirable query results as the number of queries increases. In this paper, we propose a novel equi-join scheme, One-Time Join Cross-Tags (OTJXT), which can avoid additional result leakage in multiple queries and extend to equi-join as opposed to natural join in JXT. Specifically, we design a new data encoding method using nonlinear transformations that reveals only the union of results for each query without extra leakage observed in JXT. Moreover, OTJXT addresses the linear search complexity issue (Shafieinejad et al. ICDE 2022) while preventing multiple query leakage. Finally, we implement OTJXT and compare its performance with JXT and Shafieinejad et al.'s scheme on the TPC-H dataset. The results show that OTJXT outperforms in search and storage efficiency, achieving a$\mathbf {98.5\times }$(resp.,$\mathbf {10^{6}\times }$) speedup in search latency and reducing storage cost by 62.5% (resp., 78.5%), compared to JXT (resp., Shafieinejad et al.'s scheme). Using OTJXT, a TPC-H query on a 40 MB database only takes 21 ms.

With the growing popularity of cloud computing, more and more enterprises and individuals tend to store their sensitive data on the cloud in order to reduce the cost of data management. However, new security and privacy challenges arise when the data stored in the cloud due to the loss of data control by the data owner. This paper focuses on the techniques of verifiable data storage and secure data deduplication. We firstly summarize and classify the state-of-the-art research on cloud data storage mechanism. Then, we present some potential research directions for secure data outsourcing.