Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions.

Blockchain has recently gained massive attention thanks to the success of cryptocurrencies and decentralized applications. With immutability and tamper-resistance features, it can be seen as a promising secure database solution. To address the need of searches over blockchain databases, prior work vChain proposed a novel verifiable processing framework that ensures query integrity without maintaining a full copy of the blockchain database. It however suffers from several limitations, including linear-scan search performance in the worst case and impractical public key management. In this paper, we propose a new searchable blockchain system, vChain+, that supports efficient verifiable boolean range queries with additional features. Specifically, we propose a sliding window accumulator index to achieve efficient query processing even for the worst case. We also design an object registration index to enable practical public key management without compromising the security guarantee. To support richer queries, we employ optimal tree-based indexes to index both keywords and numerical attributes of the data objects. Several optimizations are also proposed to further improve the query performance. Security analysis and empirical study validate the robustness and performance improvement of the proposed system. Compared with vChain, vChain+ improves the query performance by up to 913x.

The eruption of a pandemic, such as COVID-19, can cause an unprecedented global crisis. Contact tracing, as a pillar of communicable disease control in public health for decades, has shown its effectiveness on pandemic control. Despite intensive research on contact tracing, existing schemes are vulnerable to attacks and can hardly simultaneously meet the requirements of data integrity and user privacy. The design of a privacy-preserving contact tracing framework to ensure the integrity of the tracing procedure has not been sufficiently studied and remains a challenge. In this paper, we propose P2B-Trace, a privacy-preserving contact tracing initiative based on blockchain. First, we design a decentralized architecture with blockchain to record an authenticated data structure of the user's contact records, which prevents the user from intentionally modifying his local records afterward. Second, we develop a zero-knowledge proximity verification scheme to further verify the user's proximity claim while protecting user privacy. We implement P2B-Trace and conduct experiments to evaluate the cost of privacy-preserving tracing integrity verification. The evaluation results demonstrate the effectiveness of our proposed system.

Similarity search is a primitive operation in various database applications. Thus far, a large number of access methods have been proposed to accelerate the similarity query processing. Nonetheless, these methods mostly focus on developing standalone systems by proposing new indices. Given the fact that existing RDBMS merely support traditional indices, it is of great necessity and practical importance to develop a standard RDBMS built-in index based approach to speeding up the query processing. In this demonstration, we introduce MSQL+, a plugin toolkit that enable users to answer similarity queries in metric spaces simply using standard SQL statements. This toolkit can help existing RDBMS to effectively and efficiently handle with big data due to the following three advantages. First, MSQL+ enables users to find similar objects by submitting SELECT-FROM-WHERE statements so that it can be easily integrated into existing RDBMS. Second, MSQL+ works in a more general data space. Objects of any type can be indexed by B + -trees and the query processing can be boosted by using index seeks, as long as the similarity function is metric. Third, MSQL+ supports the parallelization of both pre-processing and query processing in distributed RDBMS.