Cross-chain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration services for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) solutions for cross-chain data exchange across multiple domains are constrained by synchronous networks, hindering efficient data exchange in intermittent network environments. Furthermore, there is a lack of research on asynchronous cross-chain transaction pool mechanisms, which are crucial for optimizing system utility. In this paper, we propose AC-BaaS, anasynchronouscross-blockchainasaservice framework tailored for the multi-domain IoT. Built upon a specially designed asynchronous sidechain architecture, the system leverages a committee to provide AC-BaaS for data exchange across multiple IoT domains. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We further propose a heuristic for optimizing the utility of the buffer pool mechanism to strike a balance between performance and resource consumption. We also examine DAS delay size settings to trade-off security and efficiency. We analyze and prove the security of AC-BaaS, simulate asynchronous communication environments under various security levels, and conduct a comprehensive evaluation. The results show that AC-BaaS outperforms SOTA schemes, improving throughput by an average of 1.71 to 5.09 times, reducing transaction latency by 64.36% to 85.49%, and maintaining comparable resource overhead.

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants.

Privacy preservation are becoming increasingly significant in machine learning, with recent privacy regulations requiring the deletion of personal data and its impact on models. Although erasing data from storage is simple, removing the influence of data on models remains a challenge. Federated unlearning is an emerging paradigm that aims to forget the knowledge contributed by some specific data to the federated model. In this paper, we design a novel federated unlearning strategy, named FedWiper, which enables exact unlearning in federated learning by erasing specific data and its impact from the federated model. Specifically, based on the granularity of the dataset, we propose training multiple federated submodels to construct a federated unlearning framework, thereby narrowing the scope of the impact of wiped data. Furthermore, the proposed Uni-Adapter structure effectively mitigates the negative impact on model performance from diminishing the dataset scale, while also reducing communication cost. Rather than focusing solely on achieving indistinguishability unlearning of the model for classification task, we extend FedWiper to unlearning for multiple types of tasks and achieve the exact unlearning. Experiments demonstrate that FedWiper can not only accelerate federated unlearning, but also achieve exact unlearning across multiple types of tasks in federated learning while ensuring minimal loss of model performance. Our Code: https://github.com/grey1989/FedWiper.

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.

As a promising implementation model of payment channel network (PCN), payment channel hub (PCH) could achieve high throughput by providing stable off-chain transactions through powerful hubs. However, existing PCH schemes assume hubs are preplaced in advance, not considering payment requests' distribution and may affect network scalability, especially network load balancing. In addition, current source routing protocols with PCH allow each sender to make routing decision on his/her own request, which may have a bad effect on performance scalability (e.g., deadlock) for not considering other senders' requests. This paper proposes a novel multi-PCHs solution with high scalability. First, we are the first to study the PCH placement problem and propose optimal/approximation solutions with load balancing for small-scale and large-scale scenarios, by trading off communication costs among participants and turning the original NP-hard problem into a mixed-integer linear programming (MILP) problem solving by supermodular techniques. Then, on global network states and local directly connected clients' requests, a routing protocol is designed for each PCH with a dynamic adjustment strategy on request processing rates, enabling high-performance deadlock-free routing. Extensive experiments show that our work can effectively balance the network load, and improve the performance on throughput by 29.3% on average compared with state-of-the-arts.

Searchable encryption (SE) has emerged as a cryptographic primitive that allows data users to search on encrypted data. Most existing SE schemes usually delegate search operations to an intermediary such as a cloud server, which would inevitably result in single-point failure, privacy leakage, and even untrustworthy results. Several blockchain-based SE schemes have been proposed to alleviate these issues; however, they suffer from some issues, such as the support for multi-keyword multi-owner model, query privacy and data storage availability. In this paper, we propose BPMS, blockchain-based privacy-preserving multi-keyword search in multi-owner setting, which supports searching over encrypted data in trustworthy, private and efficient manners. The attribute Bloom filter has been introduced into our BPMS to build indexes, which protects query privacy and improves index generation performance. To guarantee data storage availability, our BPMS leverages the advantages of IPFS (InterPlanetary File System) to store large scale of encrypted data. Security proof and comparative analysis in theory indicate that our BPMS is more secure and efficient. A series of experiments conducted on a real-world dataset further demonstrate that our BPMS is feasible in practice.

Federated learning (FL) is essentially a distributed machine learning paradigm that enables the joint training of a global model by aggregating gradients from participating clients without exchanging raw data. However, a malicious aggregation server may deliberately return designed results without any operation to save computation overhead, or even launch privacy inference attacks using crafted gradients. There are only a few schemes focusing on verifiable FL, and yet they cannot achieve collusion-resistant verification. In this paper, we propose the first Verifiable, Collusion-resistant, and Dynamic FL (VCD-FL) to tackle this issue. Specifically, we first optimize Lagrange interpolation by gradient grouping and compression for achieving efficient verifiability of FL. To protect clients’ data privacy against collusion attacks, we propose a lightweight commitment scheme using irreversible gradient transformation. By integrating the proposed efficient verification mechanism with the novel commitment scheme, our VCD-FL can detect whether or not the aggregation server is involved in collusion attacks. Moreover, considering that clients might go offline due to some reason such as network anomaly and client crash, we adopt the secret sharing technique to eliminate the effect of federation dynamics on FL. To the best of our knowledge, this is the first work to achieve collusion-resistant verification and collusion attack detection with supporting the correctness, privacy, and dynamics. Finally, we theoretically prove the effectiveness of our VCD-FL, make comprehensive comparisons, and conduct a series of experiments on MNIST dataset with MLP and CNN models. The theoretical proof and experimental analysis demonstrate that our VCD-FL is computationally efficient, robust against collusion attacks, and able to support the dynamics of FL.

Smart grid is gaining more and more attention as one of the typical applications of Internet of Things. However, in a distributed environment, how to guarantee the privacy of users in electricity trading while ensuring the efficiency of the transactions is one of the urgent issues to be solved. In this article, we propose a distributed attribute-based signature (DABS) scheme for distributed electricity trading, which can support users' free choice of trade objects without revealing their real identities. We construct a signature generation and verification method by taking advantage of the open and hard-to-tamper properties of blockchain to achieve signature verifiability independent of dynamic changes in attributes.To improve the update efficiency, we propose an improved scheme that enables the update complexity to be reduced from$O(n)$to$O(\log n)$, where$n$is the number of users. Finally, performance analysis and simulation experiments demonstrate the security and practicality of the DABS.

With the economic development of Ethereum, the frequent security incidents involving smart contracts running on this platform have caused billions of dollars in losses. Consequently, there is a pressing need to identify the vulnerabilities in contracts, while the state-of-the-art (SOTA) detection methods have been limited in this regard as they cannot overcome three challenges at the same time. (i) Meet the requirements of detecting the source code, bytecode, and opcode of contracts simultaneously; (ii) reduce the reliance on manual pre-defined rules/patterns and expert involvement; (iii) assist contract developers in completing the contract lifecycle more safely,e.g., vulnerability repair and abnormal monitoring. With the development of machine learning (ML), using it to detect the contract runtime execution sequences (called instances) has made it possible to address these challenges. However, the lack of datasets with fine-grained sequence labels poses a significant obstacle, given the unreadability of bytecode/opcode. To this end, we propose a method named VulHunter that extracts the instances by traversing the Control Flow Graph built from contract opcodes. Based on the hybrid attention and multi-instance learning mechanisms, VulHunter reasons the instance labels and designs an optional classifier to automatically capture the subtle features of both normal and defective contracts, thereby identifying the vulnerable instances. Then, it combines the symbolic execution to construct and solve symbolic constraints to validate their feasibility. Finally, we implement a prototype of VulHunter with 15K lines of code and compare it with 9 SOTA methods on five open source datasets including 52,042 source codes and 184,289 bytecodes. The results indicate that VulHunter can detect contract vulnerabilities more accurately (90.04% accurate rate and 85.60% F1 score), efficiently (only took 4.4 seconds per contract), and robustly (0% analysis failed rate) than the SOTA methods. Also, it can focus on specific metrics such as precision and recall by employing different baseline models and hyperparameters to meet the various user requirements,e.g., vulnerability discovery and misreport mitigation. More importantly, compared with the previous ML-based arts, it can not only provide classification results, defective contract source code statements, key opcode fragments, and vulnerable execution paths, but also eliminate misreports and facilitate more operations such as vulnerability repair and attack simulation during the contract lifecycle.

Collaborative learning has been used to train a joint model on geographically diverse data through periodically sharing knowledge. Although participants keep the data locally in collaborative learning, the adversary can still launch inference attacks through participants’ shared information. In this article, we focus on the property inference attack during model training and design a novel defense mechanism, namely, NOSnoop, to defend such an attack. We propose a collaborative meta-learning architecture to learn the common knowledge over all participants and utilize the natural advantage of meta-learning to hide the sensitive property data. We consider both irrelevant property and relevant property preservation in NOSnoop. For irrelevant property preservation, we utilize the inherent advantage of meta-learning to hide the sensitive property data in meta-training support data set. Thus, the adversary cannot capture the key information related to the sensitive properties and cannot infer victim’s private property successfully. For relevant property preservation, an adversarial game is further proposed to reduce the inference success rate of the adversary. We conduct comprehensive experiments to evaluate the effectiveness of NOSnoop. When hiding the sensitive property data in meta-training support data set, NOSnoop achieves an inference AUC score as low as 0.4984 for irrelevant property preservation, meaning the adversary cannot distinguish whether the training batch has the sensitive property data or not. When preserving the relevant property, NOSnoop is able to achieve an inference AUC score of 0.5091 without compromising model utility.

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workers’ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top$N$workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workers’ privacy and the high quality of sensing services.

Traditional identity authentication solutions mostly rely on a trusted central entity, so they cannot handle single points of failure well. In addition, most of these traditional schemes need to store a large amount of identity authentication or public key information, which makes the schemes difficult to expand and use in distributed situations. In addition, the user prefers to protect the privacy of their information during the identity verification process. Due to the open and decentralized nature of the blockchain, the existing identity verification schemes are difficult to apply well in the blockchain. To solve this problem, in this article, we propose a privacy protection identity authentication scheme based on the blockchain. The user independently generates multiple-identity information, and these identities can be used to apply for an identity certificate. Authorities use the ECDSA signature algorithm and the RSA encryption algorithm to complete the distribution of the identity certificate based on the identity information and complete the registration of identity authentication through the smart contract on the blockchain. On the one hand, it can realize the protection of real identity information; on the other hand, it can avoid the storage overhead caused by the need to store a large number of certificates or key pairs. Due to the use of the blockchain, there is no single point of failure in the authentication process, and it can be applied to distributed scenarios. The security and performance analysis show that the proposed scheme can meet security requirements and is feasible.

Blockchain technology, whose most successful application is Bitcoin, enables non-repudiation and non-tamperable online transactions without the participation of a trusted central party. As a global ledger, the blockchain achieves the consistency of replica stored on each node through a consensus mechanism. A well-designed consensus mechanism, on one hand, needs to be efficient to meet the high frequency of online transactions. For example, the existing electronic payment systems can handle over 50,000 transactions per second (TPS), while Bitcoin can only handle an average of about 3TPS. On the other hand, it needs to have good security and high fault tolerance; that is, in the case when some nodes are captured by adversaries, the network can still operate normally. In this article, we establish a reputation system, called RTChain, to be integrated into the e-commerce blockchain to achieve a distributed consensus and transaction incentives. The proposed scheme has the following advantages. First, an incentive mechanism is used to influence the consensus behavior of nodes and the transaction behavior of users, which in turn influence the reputation scores of both nodes and users. That is, when a node correctly processes a transaction, it will receive the corresponding reputation value as a reward, and the reputation value will be reduced as punishment not only when the node is dishonest and violates the consensus agreement but also the transaction is not completed as required. Just like electronic transactions in the real world, the higher the reputation of the user, the more likely it is to be selected as the transaction partner. A user with a low reputation will be gradually eliminated in our system because it is difficult to complete the transaction. Second, RTChain uses a verifiable random function to generate the leader in each round, which guarantees fairness for all participants and, unlike PoW, does not consume a large amount of computing resources. Then our consensus mechanism selects the nodes with high reputation scores to reduce the number of nodes participating in the consensus, thus improving the consensus efficiency, so that RTChain’s throughput can reach 4,000TPS. Third, we built a reputation chain to implement the distributed storage and management of reputation. Finally, our consensus mechanism is secure against existing attacks, such as flash attacks, selfish mining attacks, eclipse attacks, and double spending attacks, and allows nodes that participate in the consensus to fail, as long as the reputation of the failure node does not exceed one-third of the total reputation. We build a prototype of RTChain, and the experimental results show that RTChain is promising and deployable for e-commerce blockchains.

Deep learning has aroused a lot of attention and has been used successfully in many domains, such as accurate image recognition and medical diagnosis. Generally, the training of models requires large, representative datasets, which may be collected from a large number of users and contain sensitive information (e.g., users' photos and medical information). The collected data would be stored and computed by service providers (SPs) or delegated to an untrusted cloud. The users can neither control how it will be used, nor realize what will be learned from it, which make the privacy issues prominent and severe. To solve the privacy issues, one of the most popular approaches is to encrypt users' data with their public keys. However, this technique inevitably leads to another challenge that how to train the model based on multi-key encrypted data. In this paper, we propose a novel privacy-preserving deep learning model, namely PDLM, to apply deep learning over the encrypted data under multiple keys. In PDLM, lots of users contribute their encrypted data to SP to learn a specific model. We adopt an effective privacy-preserving calculation toolkit to achieve the training process based on stochastic gradient descent (SGD) in a privacy-preserving manner. We also prove that our PDLM can achieve users' privacy preservation and analyze the efficiency of PDLM in theory. Finally, we conduct an experiment to evaluate PDLM over two real-world datasets and empirical results demonstrate that our PDLM can effectively and efficiently train the model in a privacy-preserving way.

Data exchange is one of the huge challenges in Internet of Things (IoT) with billions of heterogeneous devices already connected and many more to come in the future. Improving data transfer efficiency, scalability, and survivability in the fragile network environment and constrained resources in IoT systems is always a fundamental issues. In this paper, we present a novel message routing algorithm that optimizes IoT data transfers in a resource constrained and fragile network environment in publish-subscribe model. The proposed algorithm can adapt the dynamical network topology of continuously changing IoT devices with the rerouting method. We also present a rerouting algorithm in Message Queuing Telemetry Transport (MQTT) to take over the topic-based session flows with a controller when a broker crashed down. Data can still be communicated by another broker with rerouting mechanism. Higher availability in IoT can be achieved with our proposed model. Through demonstrated efficiency of our algorithms about message routing and dynamically adapting the continually changing device and network topology, IoT systems can gain scalability and survivability. We have evaluated our algorithms with open source Eclipse Mosquitto. With the extensive experiments and simulations performed in Mosquitto, the results show that our algorithms perform optimally. The proposed algorithms can be widely used in IoT systems with publish-subscribe model. Furthermore, the algorithms can also be adopted in other protocols such as Constrained Application Protocol (CoAP).

Dynamic spectrum access (DSA) is a promising platform to solve the problem of spectrum shortage for which the most challenging issue is spectrum allocation under uncertain availability information, which is referred as a nondeterministic spectrum allocation problem. The nature of such a problem is due to inaccurate spectrum sensing results, which are induced by that power or energy based sensing can be greatly impacted by thermal and environmental noise. For spectrum allocation, auction-based mechanisms have been extensively studied because of channel allocation efficiency, and its potential to achieve bidding truthfulness for secondary uses (SUs). However, most existing spectrum auction mechanisms focus on realizing the truthfulness under certain spectrum availability information. In this paper, we propose FORTUNE, the first truthful online auction mechanism for nondeterministic spectrum allocation by considering uncertain spectrum availability and dynamic spectrum requests. Specifically, we take limited information to compute expected income and losses when interference between primary users (PUs) and SUs occurs, and present a virtual request method for changing of spectrum's actual state. Thorough theoretical analysis proves the truthfulness of FORTUNE. Furthermore, given a sample set with 5%-30% noise in spectrum sensing, FORTUNE achieves not only truthfulness, but also up to 50% higher channel utilization than existing spectrum auction mechanisms.

The ubiquity of mobile devices has facilitated the prevalence of participatory sensing, whereby ordinary citizens use their private mobile devices to collect regional information and to share with participators. However, such applications may endanger the users' privacy by revealing their locations and trajectories information. Most of existing solutions, which hide a user's location information with a coarse region, are under k-anonymity model. Yet, they may not be applicable in some participatory sensing applications that require precise location information. The goals are seemingly contradictory: to protect a user's location privacy while simultaneously providing precise location information for a high quality of service. In this paper, we propose a method to meet both goals. Through selecting a certain number of a user's partners, it can protect the user's location privacy while providing precise location information. The user's trajectory privacy can be protected by constructing several trajectories that are similar to the user's trajectory in an interval time T. Finally, we utilize a new metric, called slope ratio, to evaluate the partners' selection algorithm that we proposed. Then, we measure the privacy level that the location and trajectory privacy protection mechanism LTPPM can achieve. The analysis and simulation results show that LTPPM can protect the user's location and trajectory privacy effectively and also provide a high quality of service in participatory sensing. Copyright © 2012 John Wiley & Sons, Ltd.

The ubiquity of the various cheap embedded sensors on mobile devices, for example cameras, microphones, accelerometers, and so on, is enabling the emergence of participatory sensing applications. While participatory sensing can benefit the individuals and communities greatly, the collection and analysis of the participators' location and trajectory data may jeopardize their privacy. However, the existing proposals mostly focus on participators' location privacy, and few are done on participators' trajectory privacy. The effective analysis on trajectories that contain spatial-temporal history information will reveal participators' whereabouts and the relevant personal privacy. In this paper, we propose a trajectory privacy-preserving framework, named TrPF, for participatory sensing. Based on the framework, we improve the theoretical mix-zones model with considering the time factor from the perspective of graph theory. Finally, we analyze the threat models with different background knowledge and evaluate the effectiveness of our proposal on the basis of information entropy, and then compare the performance of our proposal with previous trajectory privacy protections. The analysis and simulation results prove that our proposal can protect participators' trajectories privacy effectively with lower information loss and costs than what is afforded by the other proposals.