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The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.

With the proliferation of cloud computing, data owners can outsource the spatial data from IoT devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this paper, we first design a Geometric Range Query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a Multi-dimensional Spatial keyword Similarity search scheme with Access Control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indexes and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.

In vehicular crowdsourcing, task requesters rely on a server to distribute spatial crowdsourcing tasks to on-road vehicular workers based on interests and locations. To protect the privacy of the interests and locations, both requesters and workers prefer to encrypt the information before uploading them to the server. However, such an encryption-before-outsourcing paradigm makes it a challenging issue to conduct the task matching. In this paper, we propose a Privacy-Preserving Task Matching (PPTM) with threshold similarity search via vehicular crowdsourcing. We first propose an interest-based PPTM by transforming vehicular workers’ interests into binary vectors. By using Symmetric-key Threshold Predicate Encryption (STPE) and proxy re-encryption, PPTM achieves privacy-preserving multi-keyword task matching with Jaccard similarity search in multi-worker multi-requester setting. Furthermore, by comparing the Euclidean distances between workers and requesters against a pre-defined threshold, PPTM preserves the location privacy of workers and requesters that only reveals the comparison results to the crowdsourcing server. The security analysis and extensive experiments demonstrate that PPTM protects the confidentiality of locations and interests of requesters and workers while achieving the efficient task matching.

The combustion of double fuel droplets with different initial centre spacings and different droplet radii in convective environment are studied numerically by using the VOF (volume of fluid) model....

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

The once-through steam generator designed by Babcock & Wilcox is used as the prototype. The two-fluid three-flow-field mathematical model is introduced and the wall heat transfer conditions are re-partitioned in present work. The model is used to calculate the flow and heat transfer involving the single-phase subcooled water region, nucleate boiling region, dryout phenomenon, post-dryout region, and single-phase superheated steam region, i.e., the “full range” of the process. The results show that the proposed model could accurately predict full-range thermal-hydraulic characteristics and post-dryout deviation from thermodynamic equilibrium. The heat transfer type in the nucleate boiling region was mainly evaporation heat transfer, accompanied with liquid heat convection and quenching heat transfer. Furthermore, the heat transfer coefficient decreased and the wall temperature rose sharply at the dryout position with the heat transfer type turning into steam heat convection in the post-dryout region. Investigation of the level of deviation from thermodynamic equilibrium in the post-dryout region was conducted by defining a new parameter post-dl, with the results showing that the level of deviation decreased gradually with the development of flow and heat transfer in the post-dryout region, and the average deviation was about 42% when the once-through steam generator ran at full load.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.

A three-dimensional “unit tube” physical model is established by simplifying the once-through steam generator (OTSG) designed by B&W and a three-dimensional two-fluid three-flow-field mathematical model that takes into account the interactions between liquid droplets and other flow-fields (steam and liquid film) and the interactions between liquid droplets and the wall in liquid deficient regions is introduced. The results of a numerical simulation using the developed OSTG model conducted at different operating parameters show that the three-dimensional two-fluid three-flow-field mathematical model used in this paper can accurately predict the dryout and post-dryout wall temperature distribution of the OTSG at different operating conditions. The simulations also show that the post-dryout wall temperature decreases along the height of the heat transfer tube at low mass fluxes while gradually increases at high mass fluxes, which is opposite to the changes in wall temperature seen in post-departure from the nucleate boiling region. Furthermore, dryout occurs later as pressure increases, with a smaller soaring maximum of the wall temperature. Dryout will occur when the wall temperature gradient reaches or exceeds 250 K/m as the heat flux increases.