Industrial Internet of Things (IIoT) has provided a promising opportunity to build digitalized industrial systems. A fundamental technology of IIoT is Radio-Frequency IDentification (RFID) technique, which allows industrial participants to identify items and anchor time series IoT data for them. They can further share the IoT data through the cloud service to enable information exchange and support critical decisions in production operations. Storing IoT data in the cloud, however, requires a data access control mechanism to protect sensitive business issues. Unfortunately, using traditional cryptographic access control schemes for time series IoT data face severe efficiency and key leakage problems. In this paper, we design a secure industrial data access control scheme for cloud-assisted IIoT. Our scheme enables participants to enforce fine-grained access control policies for their IoT data via ciphertext policy-attribute based encryption (CP-ABE) scheme. Our scheme adopts a hybrid cloud infrastructure for participants to outsource expensive CP-ABE tasks to the cloud service with strong privacy guarantees. Importantly, our scheme guarantees a new privacy notion named item-level data protection for IoT data to prevent key leakage problem. We achieve these goals via several encryption and optimization techniques. Our performance assessments combine system implementation with large-scale emulations and confirm the security and efficiency of our design.

Internet of Things (IoT) is a promising technology to provide product traceability for industrial systems. By using sensing and networking techniques, an IoT-enabled industrial system enables its participants to efficiently track products and record their status during production process. Current industrial IoT systems lack a unified product data sharing service, which prevents the participants from acquiring trusted traceability of products. Using emerging blockchain technology to build such a service is a promising direction. However, directly storing product data on blockchain incurs in efficiency and privacy issues in data management due to its distributed infrastructure. In response, we propose Cpds, a Compressed and Private Data Sharing framework that provides efficient and private data management for product data stored on the blockchain. Cpds devises two new mechanisms to store compressed and policy- enforced product data on the blockchain. As a result, multiple industrial participants can efficiently share product data with fine-grained access control in a distributed environment without relying on a trusted intermediary. We conduct extensive empirical studies, and demonstrate the feasibility of Cpds in improving the efficiency and security protection of product data storage on the blockchain.

The application of blockchain to Vehicular Edge Computing (VEC) has attracted significant interests. As the Internet of Things plays an essential and fundamental role for data collecting, data analyzing, and data management in VEC, it is vital to guarantee the security of the data. However, the resource-constraint nature of edge node makes it challenging to meet the needs to maintain long life-cycle IoT data since vast volumes of IoT data quickly increase. In this paper, we propose Acce-chain, a storage-elastic blockchain based on different storage capacities at the edge. Acce-chain supports re-write operation to re-write the historical block with a newly generated block without breaking the hash links between the blocks. As a result, Acce-chain ensures that the hot data can be efficiently accessed at the edge without incurring much communication costs or increasing the total size of the chain. To guarantee the security of the re-write process, we propose a new cryptographic primitive named Dynamic Threshold Trapdoor Chameleon Hash (DTTCH). To guarantee the verifiability of query operation, we design a novel storage structure named HybridStore to ensure the verifiable query for on-chain/off-chain IoT data. As a result, Acce-chain achieves both authorized re-write and verifiable query simultaneously. We provide security analysis for the DTTCH scheme and the IoT data query algorithms. We evaluate Acce-chain through experiments and the results show that the performance of the re-write operation is feasible in real-world VEC settings, and the query efficiency can achieve up to several magnitudes better than which of the baseline. The results also demonstrate that Acce-chain can provide high service quality for the latency-sensitive VEC systems.

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers usually collect users' personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different consumers, which raises price fairness concerns. In this paper, we propose a smart price auditing system (Spas thereafter) that allows a user to purchase Fair Price Insurance in the form of Price Auditing Contract, then the price of RHS order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrated a theoretical model for practical payment flows based on real RHS user data, and we implemented Spas in Hyperledger Fabric to show that decentralizing, and automating price auditing for RHS with financial incentives is technically feasible.