The hybrid-storage blockchain relieves on-chain storage constraints by storing raw data off-chain and retaining only data hashes on-chain. An authenticated data structure (ADS) ensures secure, verifiable queries within this architecture. A significant challenge arises when on-chain nodes must update the root digest after inserting new data objects into the ADS. Most existing research has focused only on static data scenarios. This paper introduces a novel keyword query framework for encrypted data, enabling the synchronized maintenance of the ADS on-chain. The proposed ADS assigns a unique keyword to each leaf node and fixes the ADS’s tree topology based on historical data. To accommodate new data, we develop specific keyword binding and update rules. To minimize root digest updates, we separate the digest construction of the search key from the data content. The data content digest is computed using the Mercury commitment. This approach ensures the root digest remains unchanged when a new data object is added. The fixed topology makes updating the ADS’s root digest simple. Keyword binding and update rules further reduce the frequency of root digest updates associated with keywords Extensive experimental evaluation results show the superiority of our proposed method in both query time and VO size.

Graph neural networks (GNNs) have become increasingly popular in deep learning models for graph classification tasks and are being increasingly adopted in various Internet of Things (IoT) applications, such as resource allocation, anomaly detection, and traffic forecasting. However, due to the strong connectivity of graph data, privacy information such as membership inference, graph structure, and model extraction can be inferred from trained GNN models. This highlights the importance of incorporating differentially private training of GNNs for privacy protection. Previous studies have faced limited classification accuracy issues or failed to provide graph-level privacy guarantees. In this paper, we propose a noise-adaptive and fast-converging DP (NFDP) approach for GNN-based graph classification tasks. NFDP provides privacy protection while mitigating the negative impact of noise on classification accuracy by utilizing adaptive cropping and adaptive noise addition. It accelerates convergence based on an adaptive learning rate. We conducted extensive experiments with three widely used GNN models on both real-world and synthetic datasets. The experimental results show the efficiency of our proposed NFDP in terms of accuracy, sensitivity, and F1-score when compared with the DP-SGD approach. For instance, the accuracy of NFDP is improved by 17.3%, 6.33%, and 18.79% for fingerprints, ECG, and synthetic datasets, respectively.

The Internet of Things (IoT) links the physical world to computing systems, and blockchain presents an opportunity to address the issues of weak interoperability and security flaws within IoT. However, blockchain faces the challenge of low throughput and scalability. Sharding is a promising solution, but it divides the blockchain into multiple committees, making the attack cost of malicious nodes lower. Sharding also leads to a large number of cross-committee transactions, which degrades the system’s performance. In this article, we propose the NSshard sharding framework that provides secure and low-cross-committee scaling. NSshard consists of network sharding and state sharding. We first propose a reputation score-based network sharding, which assigns each node a reputation score to reward its honest verification of transactions and penalizes its malicious behavior. This network sharding uses a random but balanced distribution of reputation scores, thereby decreasing the risk of collusion. We also propose a graph-based account partitioning scheme for state partitioning. To reduce the amount of cross-committee transactions, the scheme uses an undirected weighted graph to depict accounts and transactions. We design two algorithms based on edge splitting and overlapping community discovery, respectively. We also propose a dynamic sharding method to handle new transactions. We conduct extensive experiments to evaluate the efficiency of the proposed framework based on Ethereum transaction data. The experimental results show that our proposed framework can reduce the number of cross-committee transactions by 34.8% at 128 committees compared to the Metis algorithm.

Individuals can easily generate highly realistic images using artificial intelligence-generated content technologies, which complicates the verification of images’ ownership rights. This raises potential issues such as spreading misinformation, fraud, and copyright infringement. Digital watermarking is a promising solution to protect the copyright of a digital image by embedding watermarks within it. However, many existing deep learning-based watermarking approaches struggle to simultaneously resist multiple attacks effectively and maintain the quality of watermark images. In this paper, we propose a bidirectional-interactive and context-aware (BICA) deep network designed to enhance the robustness of the watermark while maintaining the quality of the encoded image. We propose a new attention module in the encoder to improve the invisibility and robustness of the watermarked images by implementing an adaptive two-way interaction between local and global features. Additionally, we employ fine-grained downsampling to enhance the attention module’s ability to capture comprehensive feature information. Extensive experimental results demonstrate that the BICA network can embed watermark information into an image without compromising image quality. For instance, BICA has an accuracy exceeding 95% against various moderate noise attacks, with average PSNR and SSIM values of 40.4021 dB and 0.9943, respectively.

With the growing popularity of federated learning, the security of training models against backdoor attacks has become a key challenge. Existing defense schemes often fail to address the complexity and diversity of such attacks so as to make training models vulnerable. In this paper, we propose a comprehensive dual-layered model protection scheme for fog computing-based federated learning framework. In our scheme, we first introduce a multi-metric defense mechanism deployed on fog servers to defend against malicious backdoor attacks from edge devices. The proposed defense mechanism employs multiple detection indicators to simultaneously evaluate and detect gradient and model training attributes, so that the abnormal local gradients are identified effectively. Further, we construct a second-layered defense scheme deployed on aggregation servers to regularly monitor the participation status of fog servers, whose purpose is to detect the distribution of uploaded gradients and eliminate malicious gradients from compromised fog servers. Additionally, we design an adaptive gradient adjustment method to mitigate the influence of deleting malicious gradients on the global model training process. Experimental results show that our dual-layered model protection scheme can perform well against three type of backdoor attacks (BadNet, Blended and WaNet).

Sharding is a promising solution to deal with the low throughput and poor scalability issues of the blockchain system. It horizontally scales the blockchain by separating the network into several sub-networks known as shards and allowing for parallel transaction processing across shards. However, sharding introduces a significant number of cross-shard transactions, which severely decreases system performance. Previous research has focused on static sharding, which assigns accounts with frequent trading activity to the same shard once and for all. Unfortunately, accounts’ trading behavior is not constant; the initial account sharding becomes ineffective as the blockchain system runs over time. In this paper, we proposed DATS, a novel dynamic sharding scheme designed to facilitate cost-efficient account adjustments in blockchain. We propose a transaction-driven account model where each account consists of multiple sub-accounts in different shards. By resetting the status of sub-accounts via a migration transaction, we can make account adjustments at a low cost and convert cross-shard transactions into intra-shard transactions. Moreover, we develop a time-series prediction model to determine the shards where accounts should be located by forecasting the volume of cross-shard transactions from each sender. We also propose a mechanism for handling hot shards by redirecting transactions to less burdened shards while maintaining workload balancing. We validate our proposed approach through extensive experiments using Ethereum data. The results demonstrate that DATS can lower the amount of cross-shard transactions by 80% in a scenario involving 24 shards.

Sharding is a promising technique for increasing a blockchain system’s throughput by enabling parallel transaction processing. The main challenge of state sharding lies in ensuring the atomicity verification of cross-sharding transactions, which results in double communication overhead and increases the transaction’s confirmation time. Previous research has primarily focused on developing cross-shard protocols for the fast and reliable validation of transactions involving multiple shards. These studies typically generate a large number of cross-shard transactions because they primarily use simple address mapping for state sharding, that is, the prefix/suffix of the account address. In this article, we propose a state sharding scheme via density-based partitioning of the account-transaction graph. In order to reduce cross-shard transactions, the scheme groups correlated accounts into the same shard by generating the densest subgraphs, as the graph density describes the correlation among accounts, i.e., how often transactions have occurred among accounts. We formulate the graph density-based state sharding problem, with the goal of maximizing the average density across all shards under the workload constraint. We prove the NP-completeness of the problem. To reduce the complexity of finding the densest subgraph, we propose the pruning-based algorithm that reduces the search space by pre-pruning some invalid edges based on the concept of core number. We also extend the linear deterministic greedy algorithm and PageRank algorithm to handle new transactions in the dynamic scenario. We conduct extensive experiments using real transaction data from Ethereum. The experimental results demonstrate a strong correlation between the shard density and the number of cross-shard transactions, and the pruning-based algorithm can reduce the running time by an order of magnitude.

Blockchain technology, which eliminates the need for intermediaries or centralized control, offers a decentralized and transparent platform for distributed data storage and exchange. With the widespread application of blockchain, the amount of data stored on blockchain is rapidly increasing. As data is considered a potential commercial resource, it is critical to provide secure data query services in blockchain. In this paper, we focus on the dynamic skyline query, which is important in multi-objective decision-making, and propose a query framework to support efficient query processing and the authentication of dynamic skyline results. The key technique is the authenticated data structure (ADS) called the Grid-based Verkle tree (GV tree). It combines the grid-based index and the vector commitment. The minimum bounding rectangle (MBR) is used as the search key of the ADS. By pruning cells of the grid that are dynamically dominated by a data point, the GV tree filters out non-skyline points in a batch, thereby promoting query efficiency. By using the vector commitment, the GV tree enables the verification that data points are not tampered with using a small-size verification object. We present schemes for GV tree-based dynamic skyline query processing and result verification. We conducted extensive experiments, and the experimental results showed promising results for the query framework.

The potential value of data for comprehensive analysis and decision-making drives data trading. Because data can be considered private assets, data query processing must be done securely and privately. However, none of the previous work provides a supervision mechanism for secure execution, such as traceability. In practice, traceable query processing is imperative, since we can re-execute queries from some steps, instead of restarting from the beginning. In this paper, we propose a blockchain-based framework to enable secure and traceable skyline queries. For traceable query processing, we use blockchain to make a consensus on each query step and record the intermediate results of each step into ledgers. For secure query processing, we propose a secure dominance computation scheme so that data distribution will not be exposed although skyline queries involve arithmetical computation. The scheme utilizes scalar-product-preserving encryption for inequality identification in dominance tests. The inequality identification operator is conducted by two non-colluding blockchain networks collaboratively so that intermediate results do not leak knowledge about both data and query. Based on the dominance computation scheme, we present two secure skyline query protocols. Security analysis and extensive experiments show that our proposed protocols achieve promising results.

Abstract Data transmission between smart meters and data center is facing network security threats in advanced metering infrastructure of smart grid. The traditional solution is to move the data to the data center to build a centralized attack detection model, or divide the collected data into several independent and identically distributed datasets to build a distributed attack detection model. However, the long‐distance transmission and the centralized storage of data not only increase the communication overhead and time overhead, but also increase the risk of being attacked, causing privacy disclosure during the process of building the model. In this paper, we propose an efficient intrusion detection framework Fed_ADBN based on federated attention deep belief network and client selection. Clients cooperate with the data center to jointly build a horizontal federated learning framework. Under the premise of protecting data security by keeping data on the clients, we design a client selection algorithm based on client computing power, communication quality and security risks, which can improve the operating efficiency of federated learning. We also deploy a deep belief neural network with attention mechanism in each client to accurately detect possible network attacks in AMI network in real time. Experimental results show that compared with state‐of‐the‐art methods, the proposed framework can not only maintain good detection accuracy but also protect privacy.

The data query service is urgently required in sustainable-storage blockchain, where full nodes store the entire transaction data while light nodes only store block headers. Queries invariably seek data with multiple attributes. However, no existing method provides a unified authenticated data structure (ADS) to support complex query operators (e.g., range queries and data object queries) on multiattribute blockchain data. In this paper, we propose a framework EAQ that effectively supports both fast data queries on multiple attributes and authentication of the query result. We propose a new ADS, called the MR$^{Bloom}$-tree, based on the Bloom filter (BF) and Merkle R-tree. We prove the decomposability of BFs, which enables the BF to be seamlessly incorporated with the Merkle R-tree. This ADS enables range-level search using multidimensional attribute ranges and object-level search using BFs. This ADS also supports querying and proving inexistent data objects. To reduce storage overhead, we improve the MR$^{Bloom}$-tree using the suppressed BF structure, which constructs only one BF independent of the number of attributes. To manage string attributes, we transform them into discrete numerical attributes using density-based clustering to represent similar items with close numerical values. Experiments show that the proposed framework achieves promising results.

With the great development in smart devices, mobile crowdsensing (MCS) has been an innovative paradigm for data gathering. Task assignment is a fundamental problem in MCS systems and applications. Previous studies only focused on the assignment of individual tasks, neglecting planning the task processing from a higher level, e.g., making assignments between task locations and workers, which impacts the crowdsensing performance adversely. Furthermore, task characteristics, e.g., route distance, task similarity, and task priority, have a great impact on the rationality of a visiting order and the quality of crowdsensing services. In this article, we tackle the problem of rational task assignment and path planning for MCS, which aims to assign a set of task locations to a set of workers and generate location visiting sequences. We measure the assignment rationality by taking into account geographical information and task characteristics, i.e., route distance, task similarity, and task priority. We prove that the problem of computing the rationality maximization task assignment is NP-hard. For the single-worker scenario, we reduce the problem to a simpler problem with respect to only route distance and task priority criteria since the similarity measurement has a fixed value. We propose an effective greedy algorithm. For the multiple-worker scenario, we first extend the greedy idea by considering all three criteria and then propose an effective approach by reducing the computational complexity of similarity measurement. Extensive experiments show that our proposed approaches achieve promising results.

The heavy storage problem has become a key obstacle to the application of blockchain to the actual business environments, because each node needs to keep a complete replica of blockchain data. The data volume grows undesirably large in practice. It prevents the widely used devices, e.g., tablets and mobile phones, to join blockchain systems due to their limited storage and computing resource. Previous work addressed the storage issue by allowing participating nodes to only keep a fraction of the entire transaction set, e.g., sharding. However, existing studies focus on transaction placement with the minimum cross-shard communications. These studies neglect the node characteristics (e.g., storage capacity, cost, and response capability), which impacts the storage performance adversely. In this paper, we propose EBSF, a block storage framework that achieves efficient block storage by constructing a block allocation plan based on node characteristics. Blockchain nodes are organized into committees such that nodes in a committee work together to maintain the entire blockchain data. We formulate the block allocation plan problem that assigns each block to at least one node in a committee. The goal is to minimize the total cost while reaching the threshold of the response capability of each block. We prove the NP-hardness of the problem and propose heuristic algorithms. We also propose two strategies to handle the dynamic scenario of new blocks. Extensive evaluation shows the efficiency and effectiveness of the proposed framework.

Vehicular networks, which are recognized as an innovative technology for information collection due to the powerful sensing capability and strong mobility, have been an important platform for geographic crowdsourcing services and are particularly useful in environmental data collection. Crowdsourced data collection in vehicular networks must be performed in a communication-efficient manner due to the resource-constrained wireless communication links. Moreover, it is a pressing problem to improve the quality of responses because workers may provide data of poor quality or even fabricate data to defraud rewards. While the existing work has focused on spatial/temporal task coverage and monetary rewards, in this work, we propose a cost-efficient framework for crowdsourced data collection in vehicular networks while ensuring the accuracy of the response. Crowdsourced data collection consists of two main steps: 1) task assignment and 2) crowdsourced answer gathering. We first propose a task assignment scheme that maximizes the overall data quality and reduces the amount of data transmission. We estimate the data quality level based on a Gaussian mixture model and reduce the amount of data transmission by carefully selecting a subset of vehicles for crowdsourced tasks. We then design an answer gathering scheme that considers both the length of the aggregation tree and data delivery and minimizes the communication cost for collecting answers from participants. Extensive experiments on both synthetic data sets and real data sets show that our proposed framework achieves promising results.

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average.

Fog computing is mainly used to process a large amount of data produced by terminal devices. As fog nodes are the closest acquirers to the terminal devices, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or aggregated. When some applications need to require real-time process with high security, cloud service may sample some data from fog service to check final results. In this paper, we propose a secure data query framework for cloud and fog computing. We use cloud service to check queried data from fog network when fog network provides queried data to users. In the framework, cloud server pre-designates some data aggregation topology trees to fog network, and then fog network may acquire related data from fog nodes according to one of the pre-designated data aggregation trees. Additionally, some fog nodes are assigned as sampled nodes that can feed back related data to cloud server. Based on the security requirements of fog computing, we analyze the security of our proposed framework. Our framework not only guarantees the reliability of required data but also effectively protects data against man-in-the-middle attack, single node attack and collusion attack of malicious users. Also, the experiments show our framework is effective and efficient.

Data aggregation is a fundamental operation in Internet of Things (IoT) applications, e.g., distributed Internet-based industrial control and computing systems. As IoT devices are increasingly connected to the system via resource-constrained wireless communication links, it is critical to perform communication-efficient data aggregation to answer complex queries (e.g., skyline queries and equality joins) from IoT applications. In this paper, we investigate the problem of constructing an aggregation tree (AT) for complex queries with the minimum communication cost. As complex queries have a dynamic size of intermediate results, existing Steiner tree-based approaches for traditional query operators, e.g., MIN and top-${k}$ , cannot be directly applied. We first formalize the aggregation gain by jointly considering the data pruning power (the size of data points that can be pruned during the aggregation for complex queries) and aggregation cost (the size of data points transmitted for the aggregation). By maximizing the aggregation gain, the data set that has a higher pruning power and a smaller size is selected and transferred for data aggregation at succeeding nodes. We then propose to construct the AT by connecting a set of aggregation operations with maximum aggregation gain. Extensive evaluation shows that our proposed framework achieves the promising results.

Storage nodes in two-tiered sensor networks are responsible for storing sensor-collected data and processing the sink-issued queries. Therefore, storage nodes are vulnerable to attack because of their importance. In this paper, we propose a privacy and integrity preserving protocol called SSQ, which is able to prevent compromised storage nodes from leaking sensitive data and allows the sink to detect the misbehaviors of compromised storage nodes. For privacy preserving, a size-limited bucketing technique is proposed to mix the data in a range, and a prefix membership verification technique based on Bloom filters is developed to perform skyline queries on encrypted data items. For integrity preserving, a Merkle hash tree-based technique is investigated to prevent compromised storage nodes from tampering and dropping data. Detailed performance evaluations confirm the high efficacy and efficiency of SSQ. Copyright © 2013 John Wiley & Sons, Ltd.