Permission delegation in access control provides the subject with a second method to obtain object permissions in addition to permission granting. It is especially applicable when the owner and manager of the object are inconsistent. With the development of the Internet of Things, there are more and more scenes where object owners and managers are inconsistent, but the research on permission delegation in access control based on blockchain is not perfect. Therefore, once implemented in these blockchainbased access control algorithms, the permission delegation tends to have an unauthorized access. Based on the analysis of the causes for the unauthorized access vulnerability, this paper proposes a token-constrained permission delegation algorithm (TCPDA), which converts the access control policy corresponding to permissions into constraints for permission use, embeds the constraints in the permission token, and forms constraints on the transfer of tokens. Only subjects that meet the constraint conditions can receive tokens, thereby solving unauthorized access vulnerability caused by permission delegation. Since not all access control models can transform strategies into constraints and integrate them into blockchain tokens, this paper also proposes a permission delegation algorithm for decision-making entities to make desirable decisions. Finally, the security analysis shows that the two proposed schemes can overcome the unauthorized access vulnerability caused by permission delegation, and the algorithm performance is analyzed through experiments.

Named Data Networking (NDN) is a new clean-slate architecture for the future Internet. Efficient content retrieval is the original intention of NDN design. The content retrieval process driven by content consumers in NDN includes the following challenges, consumers do not know whether the content exists and whether the content producer is reliable. Invalid interest packets could cause the occupation of limited network resources and DoS attack problem. To ensure the authenticity and integrity of the data packets, consumers need to pre-configure the trust schema, which is centralized and prone to the single point of failure problem. Blockchain has widespread attention to build trust in a distributed way, and Ethereum is a programmable blockchain, a decentralized smart contract platform. To lighten the burden of consumers, we proposed a Smart Contract-based Trusted Content Retrieval Mechanism (SCTCRM) for NDN in this paper. The mechanism contains a trustworthy information base for content and producers based on smart contracts, and provides content retrieval and name resolution services for content consumers. The purpose of this mechanism is to improve the efficiency and security of content retrieval process. We described the framework and the workflow of SCTCRM, and used Colored Petri Nets to create a formal mathematical model and analyze the security of the mechanism. Finally, the cost of storage and Gas in smart contracts are evaluated through the prototype deployment. From the results, we can see that the proposed mechanism is security and practicality.INDEX TERMS Named data networking, blockchain technology, smart contract, trusted content retrieval, Colored Petri Nets.

In recent years, Vehicular Ad Hoc Network (VANET) has developed significantly. Coordination between vehicles can enhance driving safety and improve traffic efficiency. Due to the high dynamic characteristic of VANET, security has become one of the challenging problems. Trust of the message is a key element of security in VANET. This paper proposes a Manhattan Distance Based Trust Management model (MDBTM) in VANET environment which solves the problem in existing trust management research that considers the distance between the sending vehicle and event location. In this model, the Manhattan distance and the number of building obstacles are calculated by considering the movement relationship between the sending vehicle and event location. The Dijkstra algorithm is used to predict the path with the maximum probability, when the vehicle is driving toward the event location. The message scores are then calculated based on the Manhattan distance and the number of building obstacles. Finally, the scores are fused to determine whether to trust the message. The experimental results show that the proposed method has better performance than similar methods in terms of correct decision probability under different proportions of malicious vehicles, different numbers of vehicles, and different reference ranges.

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