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Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.

With dramatic developments of blockchain technology, a number of blockchain-based applications emerge rapidly, among which the incorporation of blockchain into Internet of Things is one of the most valued research direction. Such powerful incorporation is a double-sided sword, i.e., it can benefit both individuals and society but has the vulnerability to coin hopping attack that is a new type of pool mining attack and hard to happen in traditional blockchain networks. In this paper, we theoretically prove the feasibility of coin hopping attack, deeply analyze the conditions of attack implementation, and comprehensively investigate the impacts of coin hopping attack. Moreover, some defense strategies are addressed. To our best knowledge, this paper is the first work targeting coin hopping attack.

With the prosperous deployment of IoT devices in recent years, more data are collected by ubiquitous sensors. Hence, there are two main challenges needed to be solved. Firstly, how to accommodate the communication links among these devices and collect their data effectively? Secondly, how to track or localize these devices and well organize the IoT network? The common way to track these devices is achieved by registering locations of these devices manually or RSSI measurement. However, these measures suffer from high complexity and inaccuracy, and cause boring reconfiguration process with shift of devices. In this framework, we proposed a novel and low cost IoT monitoring system with self-location awareness. The collected data by sensors, such as the light intensity, can be used for device localizations. By using the proposed algorithm, we derived the critical parameters for light curves, and interpolated for the predicted light intensity inside the whole room. The experiment results indicated the proposed algorithm is useful for inferring device location with low cost, which are suitable for device management without privacy information.

Federated learning becomes popular for it can train an excellent performance global model without exposing clients’ privacy. However, most FL applications failed to consider there exists fake local trained models returned from attackers or dishonest users. Not only would the fake parameters be harmful to the convergence of the global model but also be wasting of other users’ computational resources. In this paper, we propose a framework to grade the users’ credit score based on the performances of the returned local models on the testing dataset. We also consider historical data using the exponential moving average to give a relatively higher weight for the most recent testing results. The experiments show that our system can efficiently and effectively find out the fake local models and then speed up the convergence of the global model.

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

The past three years have seen the rapid increase of Bitcoin difficulty, which has led to a substantial variance in solo mining. As a result, miners tend to join a large open pool to get a more stable reward. Nowadays, mining pools take up over 98% of Bitcoins total computation power. In a sense, this is a manifestation of Bitcoin that tends to be centralized. Thus, researchers have shown an increased interest in pool mining payoff and security. The purpose of this paper is to review and summarize recent research in Bitcoin pool mining system. We first introduce several common reward distribution schemes, and analyze their advantages and disadvantages with some improvement mechanisms; In the second section, to address pool security problems, we examined the practical utility of some existing and potential attack strategies. To study those malicious attack in details, several defense methods are collected. Finally, we make an outlook on Bitcoin future.

In the continuous location-based service (LBS) queries, the trajectory k-anonymity method that incorporates user’s moving trend is becoming popular because of its high quality of service (QoS). The existing works often apply user’s moving direction to represent the user’s moving trend. However, the query destinations of the users with closer moving directions are not necessarily closer. As a result, the users in a cloaked set may diverge with the proceeding of the continuous queries, leading to large cloaked areas as well as a reduction of QoS. Therefore, in this paper, a new concept named “location similarity of query destination (LS-QD)”, is proposed to reflect user’s global moving trend. By incorporating LS-QD, we develop a novel trajectory k-anonymity method for continuous LBS queries. Our system architecture utilizes a quadtree model with a pyramid structure to construct the user cloaked sets conveniently. Both theoretical analysis and simulation experiments are carried out to evaluate the robustness and security performance of our proposed method. Compared with other existing methods, our LS-QD based k-anonymity method can reduce the cloaking area and cloaking time while increasing the cloaking success ratio by about 40%–70%, which demonstrates its superiority in guaranteeing QoS for continuous LBS queries.

Blockchain has been treated as one of the most promising technologies to promote crowdsourcing by providing new nice features, such as decentralization and accountability. Unfortunately, some inherent limitations of blockchain have been rarely addressed by the most existing works when applying blockchain into crowdsourcing, which becomes the performance bottleneck of crowdsourcing systems. In this paper, we propose a novel hybrid blockchain crowdsourcing platform to achieve decentralization and privacy preservation. Our platform integrates with a hybrid blockchain structure, dual-ledgers, and dual consensus algorithms to ensure secure communication between the requesters and the workers. Moreover, the smart contract and zero-knowledge proof are employed to ensure automatic operation of the tasks and the protection users' privacy, respectively. Finally, we conduct experiments to confirm the efficiency of the adopted consensus algorithm on our platform by comparing it with the state-of-the-art.