Blockchain technologies have revolutionized numerous industries by providing decentralized, transparent, and immutable systems. Despite significant benefits, scalability, privacy, interoperability, and regulatory compliance pose major challenges. This review explores recent advancements in blockchain protocols, focusing on innovative approaches to scalability and transaction management, as seen in works such as SChain [1]. We also discuss decentralized finance (DeFi) challenges and solutions for enhancing blockchain interoperability, such as cross-chain transactions [2], sharding optimizations [3], and novel consensus mechanisms [4]. The paper highlights the need for continuous innovation to ensure that blockchain systems can scale efficiently and comply with evolving regulations.

Neural architecture search for graph neural networks (NAS-GNNs) can automatically discover task-specific GNN architectures with performance comparable to or even superior to that of manually designed GNNs (Manual-GNNs). However, existing studies pay limited attention to the associated privacy risks and corresponding defense mechanisms. In this paper, we systematically investigate the privacy risks of NAS-GNNs and explore defense mechanisms from two perspectives: the search-result and search-process levels. For the search-result, we study the search-result-oriented privacy inference attack by adapting three privacy attacks originally developed for Manual-GNNs to NAS-GNNs, including model stealing, link stealing, and membership inference attacks. Experimental results indicate that, although NAS-GNNs generally exhibit stronger privacy robustness than Manual-GNNs, they still face non-negligible privacy risks. For the search-process, we propose a search-trajectory-oriented architecture inference attack that exploits candidate architecture-validation performance pairs to infer high-performing architectures. Experimental results demonstrate that this attack can infer promising candidate architectures. To mitigate these risks, we develop a fine-grained defense mechanism against the search-result-oriented privacy inference attack, which is further complemented by an operator-preference-oriented defense mechanism. Experimental results verify that these defense mechanisms can reduce attack performance while largely preserving task performance. We also propose a validation-performance-obfuscation defense mechanism to defend against the architecture inference attack during the search process, and experimental results show that it can reduce the attacker’s ability to infer high-performing architectures. Overall, this work provides a systematic understanding of privacy risks in NAS-GNNs and offers novel insights into their privacy protection.

Prediction queries have revolutionized data search by integrating machine learning models and traditional data processing operations for advanced analytics. However, existing prediction query frameworks for outsourced databases face a critical security vulnerability: data flows are processed in plaintext on semi-honest servers, making them susceptible to data breaches. The main challenge in achieving secure prediction queries is that machine learning inference and data processing operations are distinct functionalities, while most current cryptographic frameworks support only a single type of operation on specific encrypted data. To bridge this crucial gap, we propose SecPQ, the first framework tailored for secure prediction queries. Our approach unifies decision tree pipelines and data processing operations, such as selection, projection, and equality-joining, through equality matching on encrypted outsourced data. This enables the design of secure prediction queries with decision tree pipelines operating on encrypted data. We provide formal security definitions and proofs for SecPQ. To further optimize the efficiency of secure prediction queries, we leverage order-preserving encryption to construct SecP(Qope), which offers improved query efficiency at the expense of weaker security properties compared with SecPQ. Extensive experimental evaluations on billions of records demonstrate the feasibility and effectiveness of both SecPQ and SecP(Qope.)

Trading is an effective way to exchange resources such as energy, data, and computing services between Internet of Things (IoT) for mutual benefit. The emerging blockchain-enabled market facilitates the transparency and traceability of trading, but poses new challenges to the financial stability of the market. The existing blockchain-enabled market framework is designed as a completely free market, and due to the inherent characteristics of blockchain, it is difficult to take effective measures to maintain financial stability. To address this challenge, we propose a regulated blockchain-enabled market framework for IoT devices. Based on the heterogeneous interacting agent theory in economics, we study the price dynamics and equilibrium in the market. We establish a trading smart contract that supports trading among IoT devices, and provides the market regulator with interfaces to adjust the regulatable factors in the market to promote financial stability. A prototype system of our framework is implemented in Ethereum. The experimental results demonstrate the low execution cost of the smart contract, the correctness of our theoretical analysis for the market, and the effectiveness of the regulatory policies.

Decentralized Knowledge Graph (DKG) platforms allow the sharing of knowledge with multiple owners. While data owners can share their data with others by encrypting their data before sharing it, this naïve approach prevents data encrypted by different owners from being queried together, as it compromises query verifiability, an essential DKG platform feature. We propose Pistis, the first DKG platform capable of preserving ownership while also enabling verifiable SPARQL queries. Two novel techniques facilitate this: owner-managed end-to-end encryption and collaborative query verification . In Pistis, data owners thus encrypt their data individually and collaborate to construct an authenticated data structure (ADS) with a global key by means of secret sharing and secure multi-party computation. Then, by indexing KG data as ciphertext over the ADS, Pistis offers a cryptographic scheme called VO-SPARQL that facilitates verifiable queries on encrypted KG data with multiple owners. Pistis provides succinct proofs for two-stage SPARQL queries, including subgraph queries based on the ADS and aggregation on encrypted intermediate results based on a key-aggregate cryptographic primitive. A theoretical analysis and an empirical study provide detailed insight into the performance of Pistis while offering provable security.

Federated Knowledge Graph Embedding (FKGE) is an emerging collaborative learning technique for deriving expressive representations (i.e., embeddings) from client-maintained distributed knowledge graphs (KGs). However, poisoning attacks in FKGE, which lead to biased decisions by downstream applications, remain unexplored. This paper is the first work to systematize the risks of FKGE poisoning attacks, from which we develop a novel framework for poisoning attacks that force the victim client to predict specific false facts. Unlike centralized KGEs, FKGE maintains KGs locally, making direct injection of poisoned data challenging. Instead, attackers must create poisoned data without access to the victim's KG and inject it indirectly through FKGE aggregation. Specifically, to create poisoned data, the attacker first infers the targeted relations in the victim's local KG via a new KG component inference attack. Then, to accurately mislead the victim's embeddings via aggregation, the attacker locally trains a shadow model using the poisoned data and uses an optimized dynamic poisoning scheme to adjust the model and generate progressive poisoned updates. Our experimental results demonstrate the attack's effectiveness, achieving a remarkable success rate on various KGE models (e.g., 100% on TransE with WN18RR) while keeping the original task's performance nearly unchanged.

In recent years, blockchain has been widely used as a decentralized database. However, its limited scalability makes it unsuitable for large-scale applications. To overcome this challenge, high scalability cross-chain technologies have become crucial for implementing blockchain applications. While existing cross-chain technologies focus on implementing cross-chain logic, ignoring the issue of incomplete results that may arise when querying data provenance in cross-chain systems. In this work, we propose a cross-chain system that supports efficient and verifiable complete data provenance queries. We achieve this by designing an index based on a linear list and adjacent linked list to store related transactions, enabling us to perform a complete query. We also utilize vector commitments to generate verification objects indicating whether transactions are included in our index entries, ensuring verifiable integrity of query results. Furthermore, we construct an index structure based on B+ tree and Merkle tree to enhance the system’s availability and query efficiency. Our experimental results demonstrate that our scheme not only has excellent performance in terms of the cost of verification object, but it also improves query efficiency by approximately two times.

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine environment of blockchain, which eliminates sharding benefits. To tackle the challenge, this paper presents GRIDB, the first scalable blockchain database, by designing a novel off-chain cross-shard mechanism for efficient cross-shard database services. Borrowing the idea of off-chain payments, GRIDB delegates massive crossshard data exchange to a few nodes, each of which is randomly picked from a different shard. Considering the Byzantine environment, the untrusted delegates cooperate to generate succinct proof for cross-shard data exchanges, while the consensus is only responsible for the low-cost proof verification. However, different from payments, the database services' verification has more requirements (e.g., completeness, correctness, freshness, and availability); thus, we introduce several new authenticated data structures (ADS). Particularly, we utilize consensus to extend the threat model and reduce the complexity of traditional accumulator-based ADS for verifiable cross-shard queries with a rich set of relational operators. Moreover, we study the necessity of inter-shard load balancing for a scalable blockchain database and design an off-chain and live approach for both efficiency and availability during balancing. An evaluation of our prototype shows the performance of GRIDB in terms of scalability in workloads with queries and updates.

The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability. While existing studies focus on ensuring data integrity, how to ensure query verifiability - thus guarding against incorrect, incomplete, or outdated query results - remains unsolved. We propose VeriDKG, the first SPARQL query engine for decentralized knowledge graphs (DKG) that offers both data integrity and query verifiability guarantees. The core of VeriDKG is the RGB-Trie, a new blockchain-maintained authenticated data structure (ADS) facilitating correctness proofs for SPARQL query results. VeriDKG enables verifiability of subqueries by gathering global index information on subgraphs using the RGB-Trie, which is implemented as a new variant of the Merkle prefix tree with an RGB color model. To enable verifiability of the final query result, the RGB-Trie is integrated with a cryptographic accumulator to support verifiable aggregation operations. A rigorous analysis of query verifiability in VeriDKG is presented, along with evidence from an extensive experimental study demonstrating its state-of-the-art query performance on the largeRDFbench benchmark.

Blockchain has been regarded as a trusted carrier for distributed data storage. With large volumes of valuable data stored on blockchain, data query has become a major requirement. However, the existing blockchains do not provide efficient query functionality because of their deep-rooted chain structure. Blockchain database is a new direction that constructs index on top of blockchain to provide rich query functionalities. The existing works are either insecure because the query process separates from the blockchain consensus, or inscalable because all the data needs to be stored in the block. In this paper, we propose a novel semantic blockchain database called MSTDB. We design a hybrid on/off chain blockchain storage architecture in which the majority of blockchain storage is offloaded to the off-chain storage and a novel index structure named Merkle Semantic Trie (MST) is designed to be a secure and semantic bridge between on- and off-chain. Based on MST, MSTDB provides a variety of semantic query functions including multi-keyword query, range query, Top-K query, and cross-chain query. To improve the performance further, we design some index compression and query preprocessing techniques for MSTDB. Extensive experiments demonstrate the effectiveness and efficiency of our blockchain database.

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by 3.11× and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.

This chapter comprehensively reviews the secure, private and trustworthy learning methods from diverse perspectives. Motivated by the security and privacy issues, this chapter first presents the real data disclosure risks, attack scenarios and vulnerabilities of the current learning systems. Afterwards, the mainstream mitigation methods proposed to use differential privacy, homomorphic encryption, lightweight secure multi-party encryption, blockchain and trusted execution environment are reviewed by introducing the core algorithms and protocols. Following the tone of classical textbook writing style, in each sub-chapter, the advantages and limitations of the current schemes are discussed. Besides, this chapter argues that the adoption of single technique such as cryptograph brings about significant drawbacks. Therefore, the deep fusion of reviewed methods is promising research and practicing route to design secure while efficient learning protocols. The knowledge and insights shared in the chapter can motivate future efforts from our readers.

As a decentralized trusted database, the blockchain is finding applications in a growing number of fields such as finance, supply chain and medicine traceability, where large volumes of valuable data are stored on the blockchain. Currently, the mainstream blockchains employ a hybrid data storage architecture combining on-chain and off-chain storage. Real-time distributed search of mass data stored in this hybrid system is now a major need. However, previous fast retrieval schemes for the blockchain system are aimed only at on-chain data without considering their relevance to off-chain data, and thus fail to meet the requirement. In this paper, we propose an efficient blockchain data query scheme by introducing a novel Merkle Semantic Trie-based indexing technique without modifying the underlying database. A consensus on-chain index structure is constructed using the extracted semantic information of the off-chain data to create a mapping between the on-chain and off-chain data, thus enabling real-time data query both on and off the chain. Our scheme also provides multiple complex analytical query primitives to support semantic query, range query, and even fuzzy query. Experiments on three open data sets show that the proposed scheme has good query performance with shorter query latency for four different search types and offers better retrieval performance and verification efficiency than those available.

Cross-heterogeneous blockchain interactions have been attracting much attention due to their application in depository blockchains mutual access and cross-blockchain identity authentication. Trusted access across heterogeneous chains is gradually becoming a hot challenge. In order to ensure cross-blockchain trusted access, the majority of the current works focus on on-chain notaries and the relay chain model. However, these methods have the following drawbacks: 1) notaries on the chain are more vulnerable to attacks due to their high degree of centralization, which causes off-chain users to lose their trust and thus exacerbates the off-chain trust crisis; 2) although the relay model involves multiple parties in maintenance and supervision and enjoys a more robust trust, the paticipatant nodes are relatively fixed, which impose a terrible dilemma that invalid nodes cannot participate in consensus formation in a timely manner, thus progressively disrupting the connectivity of the relay across heterogeneous chains and eventually reducing the rate of trusted mutual access. In this article, we propose a novel general framework for cross-heterogeneous blockchain communication based on a periodical committee rotation mechanism to support information exchange of diverse transactions across multiple heterogeneous blockchain systems. Connecting heterogeneous blockchains through committees has a more robust trust than the notary method. In order to eliminate the impact of downtime nodes in a timely manner, we periodically reorganize the committee and give priority to replacing downed nodes to ensure the reliability of the system. In addition, a message-oriented verification mechanism is designed to improve the rate of trusted intervisit across heterogeneous chains. We have built a prototype of the scheme and conducted simulation experiments on the current mainstream blockchain for message exchange across heterogeneous chains. The results show that our solution has a good performance both in inter-chain access rate and system stability.