The rise of decentralized finance (DeFi), Web 3.0, and other blockchain-based applications has led to an increased demand for on-chain data analysis across multiple blockchains. Conducting advanced queries, such as data aggregation and correlation analysis, is essential for gaining valuable insights in this context. However, multi-chain queries pose several challenges for the querying system, including compatibility with existing blockchains, supporting diverse query types, and ensuring the integrity of query results. To tackle these challenges, we propose a novel paradigm called verifiable virtual filesystem (V2FS). V2FS extends the POSIX I/O interface, shifting the focus from verifying computation to verifying data. This innovative approach empowers query clients to leverage an off-the-shelf database engine to evaluate queries using verifiable data retrieved from an indexing service provider (ISP). Our solution ensures strong integrity guarantees and can be smoothly integrated with existing database engines to support various query types. To achieve blockchain compatibility, we utilize the DCert framework to certify blocks from different blockchains, making our system applicable to various blockchain systems. Furthermore, we propose cache-based algorithms and a bloom filter-integrated algorithm to optimize query performance and minimize network communication costs. Security analysis and empirical study validate the effectiveness and efficiency of the proposed system.

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.

Blockchain has emerged as a promising solution for secure data storage and retrieval for decentralized applications. To scale blockchain systems, a prevailing approach is to employ a hybrid storage model, where only small meta-data are stored on-chain while the raw data are outsourced to an off-chain storage service provider. The key issue for query processing in such a system is the design of gas-efficient authenticated data structure (ADS) to authenticate the query results. In this paper, we study novel ADS schemes for authenticated keyword search in hybrid-storage blockchains. We first propose the Suppressed Merkle inverted (Merkleinv) index, which maintains only a partial ADS structure on-chain that can be securely updated with a logarithm-sized cryptographic proof. Moreover, we propose a Chameleon inverted (Chameleoninv) index that leverages the chameleon vector commitment to achieve a constant maintenance cost. It is further optimized with Bloom filters to enhance the query and verification performance. We prove the security of the proposed ADS schemes and evaluate their performance using real datasets on the Ethereum platform. Experimental results show that, compared to a baseline solution, the proposed Merkleinvand Chameleoninvindexes reduce the average on-chain maintenance cost from US$10.39 down to US$2.50 and US$0.24, respectively, without sacrificing much the query performance.

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.

This demonstration presents vChain, a blockchain system that ensures query integrity. With the proliferation of blockchain applications and services, there has been an increasing demand for querying the data stored in a blockchain database. However, existing solutions either are at the risk of losing query integrity, or require users to maintain a full copy of the blockchain database. In comparison, by employing a novel verifiable query processing framework, vChain enables a lightweight user to authenticate the query results returned from a potentially untrusted service provider. We demonstrate its verifiable query operations, usability, and performance with visualization for better insights. We also showcase how users can detect falsified results in the case that the service provider is compromised.

With the pervasiveness of location-based services (LBS), spatial data processing has received considerable attention in the research of database system management. Among various spatial query techniques, index structures play a key role in data access and query processing. However, existing spatial index structures (e.g., R-tree) mainly focus on partitioning data space or data objects. In this paper, we explore the potential to construct the spatial index structure by learning the distribution of the data. We design a new data-driven spatial index structure, namely learned Z-order Model (ZM) index, which combines the Z-order space filling curve and the staged learning model. Experimental results on both real and synthetic datasets show that our learned index significantly reduces the memory cost and performs more efficiently than R-tree in most scenarios.