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Double auction is one of the most promising solutions to allocate virtual machine (VM) resources in two-sided cloud markets, which can increase the utilization rate of VM resources. However, most cloud auction mechanisms simply assume that the auctioneer is fully trusted while ignoring bid-privacy preservation and trade fairness in the process of auction. Previous studies have indicated that some cryptographic tools can be used to resolve the above issues, but the poor performance makes those techniques difficult to practice. In this paper, we propose a Secure and Fair Double AuCtion framework (named SF-DAC) for cloud virtual machines, which performs cloud auction efficiently while guaranteeing both bid privacy and trade fairness. We design secure 3-party computation protocols that support secure comparison and secure sorting, which enable us to construct a secure double auction scheme that outperforms all prior comparable solutions. Furthermore, we propose a fair trading mechanism based on smart contracts to prevent the bidders from halting the auction without financial penalties. The extensive experiments demonstrate that SF-DAC achieves an order of magnitude reduction in computation and communication costs than prior arts.

As one of the Generation IV nuclear reactors, the SCWR (supercritical water-cooled reactor) has high economy and safety margin, good mechanical properties for its high thermal efficiency, and simplified structure design. As the key component of nuclear reactor, the fuel assembly has always been the main issue for the design of the SCWR. The design of the fuel assembly for CSR1000 proposed by the Nuclear Power Institute of China (NPIC) has been optimized and presented in this study, which is composed of four subassemblies welded by four filler strips and guide thimbles arranged close together in the cross-shaped passage. Aiming at improving the hydraulic buffer performance of the cruciform control rod, the scram time and terminal velocity of control rod assembly were calculated to assess the scram performance based on the computational fluid dynamics and dynamic mesh method, and the mechanical property and neutronic performance of assemblies were also investigated. It has been demonstrated that the optimized fuel assembly had good feasibility and performance, which was a promising design for CSR1000.

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.

As a typical novel IoT(Internet of Things) architecture, fog-based IoT is promising to decrease the overhead of processing and movement of large-scale data by deploying storage and computing resources to network edges. However, since the edge-deployed fog nodes cannot be fully trustable, fog-based IoT suffers some security and privacy challenges. This paper proposes a novel privacy-preserving scheme that can implement data aggregation from a subset of devices in fog-based IoT. Firstly, our scheme identifies the subset to be aggregated by computing the Jaccard similarity of attribute vectors of the query users and IoT devices, where the local differential privacy is employed to protect the attribute vectors. In addition, we use local differential privacy truth discovery to protect the data of IoT devices and improve the accuracy of the aggregation result. Finally, experiments show that our scheme is efficient and highly available by comparing it with state-of-the-art works. Theoretical analyses demonstrate that our proposed scheme has excellent performance on both computational costs and communication costs.

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders’ private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.

As the basis of the Internet of Things (IoT), sensor networks have materialized its computation and communication capability into anything in our modern lives. Service composition provides us a promising way to cooperate various sensors to build more powerful IoT applications over sensor networks. However, the limited capability of sensor node poses great challenges not only to trustworthy service composition but also to secure data aggregation. The complex composite structure, computation-intensive evaluation, and massive data transmission become burdens for service composition in sensor networks. To overcome these issues, this paper proposes a distributed approach to enable efficient trustworthy service composition with secure data transmission in sensor networks. By analyzing dependency relationships, the rules for computing service trust and data trust are proposed based a multi-level trust model. Then, each target component service can be evaluated independently through a model checker. Moreover, an identity-based aggregate signature is introduced in the composite evaluation to guarantee the secure data transmission among different components. The experimental results show that our approach not only achieves efficient trustworthy service composition with complex invocation structures, but also reduces the costs in the secure data transmission.