Adapter-based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full-parameter fine-tuning for web-scale applications while preserving user privacy. By freezing the backbone and training only compact low-rank adapters, these methods appear to limit gradient leakage and thwart existing Gradient Inversion Attacks (GIAs). Contrary to this assumption, we show that low-rank adapters create new, exploitable leakage channels. We propose the Unordered-word-bag-based Text Reconstruction (UTR) attack, a novel GIA tailored to the unique structure of adapter-based FedLLMs. UTR overcomes three core challenges—low-dimensional gradients, frozen backbones, and combinatorially large reconstruction spaces—by: (i) inferring token presence from attention patterns in frozen layers, (ii) performing sentence-level inversion within the low-rank subspace of adapter gradients, and (iii) enforcing semantic coherence through constrained greedy decoding guided by language priors. Extensive experiments across diverse models (GPT2-Large, BERT, Qwen2.5-7B) and datasets (CoLA, SST-2, Rotten Tomatoes) demonstrate that UTR achieves near-perfect reconstruction accuracy (ROUGE-1/2 > 99), even with large batch sizes—settings where prior GIAs fail completely. Our results reveal a fundamental tension between parameter efficiency and privacy in FedLLMs, challenging the prevailing belief that lightweight adaptation inherently enhances security. Our code and data are available at https://github.com/shwksnshwowk-wq/GIA

In remote areas without continuous internet connectivity, patients often need to travel long distances to access healthcare services. Ensuring secure and efficient healthcare in remote areas has become a significant challenge. Inspired by delay tolerant networks and identity authentication protocols, we propose a security-enhanced data transmission scheme for healthcare in remote areas. The scheme leverages vehicles as data mules to address network intermittency, transporting data collected by wearable devices from remote areas to urban centers. It incorporates a novel key agreement mechanism based on Chebyshev polynomials and hash functions to protect patient privacy, along with a dynamic update method for pseudonyms and credentials to achieve lightweight anonymous authentication. Finally, we rigorously verify the security of the scheme using the Real-or-Random (ROR) model and demonstrate that it outperforms related methods in terms of performance.

Deep cross-modal retrieval, with its effective and efficient search capabilities, has gained widespread adoption in today’s media-sharing practices yet raises concerns regarding potential threats to user data privacy. The cutting-edge data-centric countermeasures usually adopt adversarial learning, i.e., laboriously crafting the proper perturbation for each image, resulting in the noticeable noise in adversarial examples that greatly undermines the aesthetic appeal of image sharing. To address this issue, we propose a novel Model-centric Cross-modal Privacy-preserving framework (MCP), wherein the pre-defined invisible backdoor is seamlessly integrated into the global retrieval model via backdoor learning, thereby effectively preventing shared images containing such triggers from being retrieved. Specifically, we introduce a simple yet effective cross-modal backdoor learning algorithm that alternately optimizes two losses: 1) a privacy-preserving loss for perturbing retrieval with a user-injected trigger and 2) the standard utility loss for maintaining normal retrieval performance. Compared to state-of-the-art methods, MCP excels in providing excellent stealthiness, manifesting in a notable improvement of approximately 100% in SSIM metrics. Furthermore, it achieves an outstanding privacy-preserving (backdoor) success rate, as evidenced by a substantial mAP reduction of 22.3% (for FashionVC), 11.5% (for NUS-WIDE), and 21.8% (for MIRFlickr-25K) in poisoned retrieval, while maintaining similar normal retrieval performance. Additionally, MCP exhibits robust resistance against potential black-box defenses (e.g., trigger filtering) and white-box defenses (e.g., fine-tuning and model pruning). The code and data are available athttps://github.com/lqsunshine/MCP.

Federated learning (FL) is a new learning paradigm that enables multiple clients to collaboratively train a high-performance model while preserving user privacy. However, the effectiveness of FL heavily relies on the availability of accurately labeled data, which can be challenging to obtain in real-world scenarios. To address this issue and robustly train shared models using distributed noisy labeled data, we propose FedDQ, a noise-robust FL framework that utilizes co-distillation and quality-aware aggregation techniques. FedDQ incorporates two key features: a noise-adaptive training strategy and an efficient label-correcting mechanism. The noise-adaptive training strategy relies on the estimation of labels' noise levels to dynamically adjust clients' training engagement, which mitigates the impact of wrong labels while efficiently exploring features from clean data. In addition, FedDQ designs a two-head network and employs it for co-distillation. The co-distillation strategy facilitates knowledge transfer among clients to share the representational capabilities. Besides, FedDQ enhances label correction to rectify improper labels through co-filtering and label correction. The experimental results demonstrate the effectiveness of FedDQ in improving model performance and handling noisy data challenges in FL settings. On the CIFAR-100 dataset with noisy labels, FedDQ exhibits a notable improvement of up to 32.4% compared to the baseline method.

Federated learning(FL), enabling multiple clients collaboratively to train a model via a parameter server, is an effective approach to address the issue of data silos. However, due to the self-interest and laziness of servers, they may not correctly aggregate the global model parameters, which will cause the final model trained to deviate from the training goal. In the existing proposals, the cryptography-based verification scheme involves heavy computation overheads. On the other hand, the replication-based verification method, relying on a dual-server architecture, can ensure the correctness of aggregation and reduce computation overheads, but incur at least twice the communication cost as that of the task itself. To address these issues, we propose a novel replication-based aggregation scheme for FL, which enables efficient verification and stronger correctness assurance. The scheme employs a main-secondary server architecture, which allows the secondary servers to partakes in aggregation tasks at a predetermined probability, consequently mitigating the validation overhead. Moreover, we resort to the game theory and design a Learning Contract to impose penalties on dishonest servers, enforcing rational servers to correctly compute global model parameters. Under the use of Betrayal Contract to prevent collusion among servers, we further design a training game to efficiently verify global model parameters and ensure their correctness. Finally, we analyze the correctness of the proposed scheme and demonstrate that the computational overhead of our scheme is$\frac{{n + 1}}{{2n}}$of the previous replication-based validation scheme, obtaining a significant reduction in communication cost, where$n$means the training rounds. Experimental results further validate our deduction.

Detecting phishing scams has become an important task in blockchain-based cryptocurrency applications. While many network representation learning-based approaches have been proposed for this task, they suffer from various issues including (1) the requirement of handcrafted features, which may not capture complex relationships and patterns in graph data, and/or (2) considering only node features while ignoring the more significant edge features, and/or (3) incapability of preserving complete network topology, which affects the generalization ability. In this paper, we propose a novel Edge-feature modeling-based Topological Graph Neural Network (ETGNN) to detect phishing scams on Ethereum, which avoids all aforementioned issues of existing approaches. Specifically, ETGNN involves two key components, one responsible for learning weighted features of nodes and edges in the Ethereum transaction graph, and the other responsible for incorporating global topological information of the graph using persistent homology. Finally, phishing scams are detected based on these two learned features. The experimental results demonstrate that ETGNN outperforms the state-of-the-art method with an improvement rate of 14.38% on F1-score.

Privacy-preserving data aggregation (PPDA) enables data availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking and homomorphic encryption, cannot simultaneously offer strong privacy preservation, fault tolerance for both smart meters and aggregators, verifiable aggregation, and lightweight encryption. To tackle these challenges, we design HTV-PRE, a homomorphic threshold proxy re-encryption scheme with re-encryption verifiability. HTV-PRE involves only linear operations and resists quantum attacks after being instanced by ideal lattices. By leveraging HTV-PRE, we propose a robust and lightweight data aggregation scheme with strong privacy preservation for smart grid. Robustness ensures fault tolerance and error detection. Even if some smart meters or aggregators are faulty, data aggregation can still work without imposing expensive computation on other smart meters or requiring additional trust assumptions. Additionally, to detect aggregators' errors, a proof for the aggregated result is presented so that anyone can verify whether the result has been correctly computed or not. The verifiable aggregation adds no computation/communication overhead on the user side. The performance evaluations demonstrate that our PPDA scheme significantly offloads computation overhead from smart meters and control center to the edge, and its user encryption is up to 4x faster than existing approaches.

Short-term residential electrical load forecasting (SRLF) as a cloud service usually requires fine-grained electricity consumption data as input. However, those data are closely related to users' lifestyles, thus bringing about privacy concerns. We adapt homomorphic encryption into temporal convolutional networks (TCN) to yield an efficient design for SRLF, named SecTCN, which preserves privacy for both user data and model parameters. First, a homomorphic-encryption-friendly model is proposed through novel Ticktock approximations. Second, secure load forecasting over the encrypted data is executed by cloud–edge collaboration. Third, a novel data representation and related ciphertext computations are proposed to accelerate forecasting, and a position shuffler is devised to protect models from equation-solving attacks. Experimental evaluations demonstrate that SecTCN reduces a root-mean-squared error by 21.75 averagely and a mean absolute percentage error by 4.22%$\text{ to } $22.16%, compared to unencrypted long short-term memory (LSTM) and TCN. On average, SecTCN requires only 1.10 s to make forecasting with 10.27 MB communication traffic.

To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts.In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers’ data are computed and transmitted without complicated encryption. To further reduce each worker’s overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers’ perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads.

Mobile edge computing emerges as a new paradigm to provide low-latency network services in the close proximity to users. Based on the network function virtualization (NFV) technology, network services can be flexibly provisioned as service function chain (SFC) deployed at edge servers. In some scenarios, such as the vehicular or UAV-assisted edge computing, the network topology varies rapidly due to the mobile edge servers, which changes the routing path between adjacent VNFs in an SFC. Migrating SFC to adapt to the frequent topology change can reduce the SFC latency, and improve the quality of users’ experience. However, frequent SFC migration will unavoidably increase the operation cost. In this paper, to optimize the system performance in a cost-efficient manner, we study the SFC migration problem in dynamic networks with a long-term cost budget constraint. We then propose the Topology-aware Min-latency SFC Migration (TMSM) method to strike a desirable balance between the SFC latency and the migration cost. Specifically, we first apply the Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization sub-problems. Since the decomposed problem is still NP-hard, a Markov approximation based heuristic is proposed to seek a near-optimal solution for each sub-problem. Compared with the rerouting-only strategy, which does not migrate any VNF, our TMSM reduces the latency by at least 21% on average in each time slot. Extensive evaluations show that the proposed algorithm achieves a better tradeoff between the SFC latency and migration cost than the baselines.

As a popular instance of sharing economy, ridesharing has been widely adopted in recent years. To use the convenient ridesharing service, riders and drivers have to share with the service provider their private trip information, which impedes users from freely enjoying the benefits of ridesharing. However, existing studies in ridesharing mainly focus on the optimization of rider-driver matching but ignore the protection of privacy of users. In this paper, we propose P2Ride, a Practical and Privacy-preserving Ride-matching scheme for ridesharing, which enables the service provider to efficiently match drivers with appropriate riders without learning the privacy of both drivers and riders. In P2Ride, we first convert the complex ride-matching computation into equality testing by leveraging overlapping partition systems, and then achieve the privacy-preserving ride-matching by designing a novel non-interactive private equality testing protocol. We prove the security of the proposed P2Ride theoretically. Moreover, a prototype of the P2Ride is implemented, and the experiment results over a real-world dataset demonstrate that the proposed P2Ride can achieve both high ride-matching accuracy and practical efficiency.

In Network Function Virtualization (NFV), a series of Virtual Network Functions (VNFs) organized in a specific order (called Service Function Chain, SFC) could offer an end-to-end network service for a network flow. Recently, with the new results of the exploration of VNF parallelism, hybrid SFC (SFC contains parallel VNFs) is proposed to reduce the SFC execution delay. However, it remains challenging and open to optimally embed multiple hybrid SFCs into the network. In this paper, we target at the optimal embedding problem of multiple hybrid SFCs with the purpose of minimizing the cost in an online scenario. Specifically, we propose SFT-Box, an online approach that can respond to hybrid SFC embedding requests in real-time. SFT-Box is designed to i) transform SFCs from the traditional sequential form to a standardized hierarchical Service Function Tree (SFT) form, ii) calculate and store the low-cost sub-solutions of embedding common SFTs, and iii) provide prompt solution response based on stored sub-solutions. To the best of our knowledge, this is the first work to address the online optimal embedding problem of multiple hybrid SFCs. With extensive evaluations, we demonstrate that, compared with the benchmark methods, SFT-Box can achieve up to 30% cost-saving and at least$22\times $latency reduction in enabling real-time response.

The skyrocketing market value of cryptocurrencies has prompted more investors to pour funds into cryptocurrencies to seek asset hedging. However, the anonymity of blockchain makes cryptocurrency naturally a tool of choice for criminals to commit smart contract scams. Consequently, smart contract scam detection is particularly critical for investors to avoid economic loss. Previous methods mainly leverage specific code logic of smart contracts and/or design rules based on abnormal transaction behaviors for scam detection. Although these methods gain success at detecting particular scams, they perform worse when applied to scams with highly similar codes. Besides, well-designed decision rules rely on expert knowledge and tedious data collection steps, which causes poor flexibility. To combat these challenges, we consider the problem of smart contract scam detection via mining topological features of account interaction information that dynamically evolves. We adopt interactive features extracted from dynamic interaction information of accounts and propose a framework named TTG-SCSD to utilize the features and Topological Data Analysis for smart contract scams detection. The TTG-SCSD constructs discrete dynamic interaction graphs for each contract and designs interactive features that characterize account behaviors. The features are modeled combined with a topology quantification mechanism to capture contract intentions in transactions. Experimental results on real-world transaction datasets from Ethereum show that TTG-SCSD obtains better generalizability and improves the performance of the bare versions of the comparison methods.

Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the clientside. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.

With the explosive growth of data, a large amount of image data is stored on cloud servers. However, cloud servers can easily collect sensitive information about stored images, which brings serious privacy issues. Although uploading encrypted images to cloud servers could solve the privacy problem, most of the existing privacy-preserving schemes inevitably reduce the accuracy and efficiency of image retrieval. To address the above challenging issues, we propose a privacy-preserving content-based image retrieval scheme based on multi-indexed hashing (MIH) in this paper. To improve the retrieval precision, the ViT model is first used to extract feature descriptors of images and ITQ method is utilized to downscale the feature vectors into binary vectors. Subsequently, based on additive secret sharing, we propose a new secure Hamming distance calculation protocol to perform similarity measure, which protects the data privacy of image features. Finally, we design a secure multi-index hash structure to filter the dataset to improve the search efficiency. Experiments on the dataset demonstrate the efficiency and security of the scheme.

Understanding graph characteristics is of great importance for graph analytics. Among the many properties, shortest path distance is the fundamental and widely used one. With the advent of cloud computing, it is a natural choice for the data owners to host their massive graphs on the cloud and outsource the shortest distance querying service to it. However, the new paradigm brings serious security concerns as graph data and shortest distance queries may contain sensitive information of data owners and users. In this paper, we propose a novel scheme to support privacy-preserving approximate shortest distance queries with advanced graph analytics over large-scale encrypted graphs, which enables an untrusted cloud to answer shortest distance queries as well as advanced graph metrics (e.g., node centrality) without knowing the content of queries and the sensitive information of outsourced graphs. Compared with the state-of-the-art solutions, our design can support not only efficient and accurate shortest distance approximation, but also advanced graph analytics. We prove that our scheme is secure under the chosen-plaintext model. Experimental results over real-world datasets show that our scheme achieves high approximation accuracy with practical efficiency.

Edge computing emerges as a new paradigm to provide low-latency network services in close proximity to end users. Based on the network function virtualization (NFV) technology, network services can be flexibly and scalably provisioned as virtual network function (VNF) chains deployed at edge servers. With such advantages, both the industry and research communities have done extensive studies on deploying VNF chains at network edge. The existing works mainly take an ideal assumption that the network is totally safe and there are no malicious users. Therefore, they leverage all available resources to serve their users. However, such an assumption is impractical in real networks. Security services, such as firewall, deep packet detection, intrusion detection, are always required for production networks. The existing service deployment methods fail to consider the co-existence of security services and functionality services. In this paper, we present the topic of joint deployment of both security and functionality services, wherein the security services are responsible to check the data flows before being processed by the functionality services. To solve this problem, we propose the Secure Deployment Pattern, which aims to simultaneously satisfy the security protection and QoS requirements at network edge. It divides the services into two kinds, i.e., the user-oriented functionality services, and the service provider-oriented security services. In this case, it is very challenging to jointly deploy the security services and functional services with respect to the resource and latency constraints. We formulate this problem as an integer programming model, and propose the heuristic algorithms to solve it. As far as we know, this paper is the first step, which targets at a proper orchestration of security and functionality services in edge computing. Extensive evaluations show that the proposed algorithms are effective and efficient, in terms of the execution time and the average number of served requests.

The data aggregation approach in smart grid (SG) is an effective solution to make data available while keeping privacy preserving at the same time. The fault tolerance means decryption can still be carried out successfully even if some smart meters (SMs) are breaking down. It is a challenging issue to design an efficient, fault-tolerant data aggregation scheme with no help of centralized trusted authority (TA) or key update after each fault recovery. Recently, a fault-tolerant data aggregation scheme FESDA (Saleemet al.,2020) was presented. However, we identify a serious and inherent vulnerability in its fault tolerance. Specifically, given an equivalent ciphertext derived from each SM’s private key aiming at resiting faults, the control center can abuse it to obtain any SM’s reading. An effective attack is launched with both theoretical proof and experimentative verification. Furthermore, to fix it and solve the challenging issue, we first design an extended Shamir’s threshold secret-sharing scheme (tSSS) with master secret security and reusability, allowing SMs to reconstruct subsequent multiple secrets without leaking their original secret shares. Then, if some SMs fail to submit data successfully, the fog node (FN) starts extra request–response interactivities among itself and a limited number of SMs. Finally, a privacy-enhanced aggregation of normal reports can also be achieved. Extensive experiments demonstrate that the majority of computation costs and communication overload to acquire fault tolerance are offload on FNs, while SMs are computationally economical.

To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers’ data are computed and transmitted without complicated encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers’ perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads.

Federated learning is a novel machine learning framework that enables multiple devices to collaboratively train high-performance models while preserving data privacy. Federated learning is a kind of crowdsourcing computing, where a task publisher shares profit with workers to utilize their data and computing resources. Intuitively, devices have no interest to participate in training without rewards that match their expended resources. In addition, guarding against malicious workers is also essential because they may upload meaningless updates to get undeserving rewards or damage the global model. In order to effectively solve these problems, we propose FIFL, a fair incentive mechanism for federated learning. FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real-time worker assessment mechanism. We evaluate the effectiveness of FIFL through theoretical analysis and comprehensive experiments. The evaluation results show that FIFL fairly distributes rewards according to workers’ behaviour and quality. FIFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. In the unreliable scenario containing attackers which destroy the model’s performance, the system revenue of FIFL outperforms the baselines by more than 46.7%.

The proliferation of various mobile devices equipped with cameras results in an exponential growth of the amount of images. Recent advances in the deep learning with convolutional neural networks (CNN) have made CNN feature extraction become an effective way to process these images. However, it is still a challenging task to deploy the CNN model on the mobile sensors, which are typically resource-constrained in terms of the storage space, the computing capacity, and the battery life. Although cloud computing has become a popular solution, data security and response latency are always the key issues. Therefore, in this paper, we propose a novel lightweight framework for privacy-preserving CNN feature extraction for mobile sensing based on edge computing. To get the most out of the benefits of CNN with limited physical resources on the mobile sensors, we design a series of secure interaction protocols and utilize two edge servers to collaboratively perform the CNN feature extraction. The proposed scheme allows us to significantly reduce the latency and the overhead of the end devices while preserving privacy. Through theoretical analysis and empirical experiments, we demonstrate the security, effectiveness, and efficiency of our scheme.

City-wide package delivery becomes popular due to the dramatic rise of online shopping. In order to speed up the package delivery process without increasing the delivery cost, a promising system has been proposed, which leverages the crowdsourced taxis. Many efforts have been done on this novel system in recent literature. However, a fundamental problem still remains open, i.e., measuring the maximum capacity of taxi-based logistics at the urban scale. In this paper, we first propose an accurate and efficient measurement mechanism to tackle this problem in the Non-stop package delivery method. The basic idea is to construct a spatial-temporal graph according to the passenger demands and calculate the maximum urban capacity by combining the results of several carefully designed max-flow problems. Then, we expand our measurement mechanism to be used in other taxi-based package delivery methods after a few adaptations, including the One-hop method and the Stop-and-wait method. At last, we evaluate our measurement mechanism and compare the maximum urban capacity of various package delivery methods with a real-world dataset from an online taxi-taking platform.

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos' spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of 33% and 18.7% on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.

City-wide package delivery has become popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gaussian distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95 percent on average during the daytime, and is at most 46.9 percent higher than those of the benchmarks.

To solve the problem that the data collected in crowdsensing systems are not reliable, a large number of truth discovery protocols have been proposed. However, most of them neglect the privacy protection existing in crowdsensing systems. Some truth discovery protocols that consider privacy only provide limited privacy protection, such as only protecting the privacy of collected data. To bridge the gap, in this paper, we propose a more comprehensive privacy-preserving truth discovery protocol that can simultaneously protect the privacy of participants and truth results. Specifically, our protocol encrypts participants' observed data based on Paillier Homomorphic Cryptosystem. Then, through the interaction between two servers, we can calculate participants' weights and estimate the truth results in the encrypted domain. Moreover, based on the data perturbation technology, the privacy of sensitive data exchanged between the two servers is protected in our protocol. Theoretical analysis and experimental results demonstrate that our protocol can effectively protect the privacy of participants and truth results without losing the accuracy of truth results.

Promising unprecedented convenience, Online Ride Hailing (ORH) service such as Uber and Didi has gained increasing popularity. Different from traditional taxi service, this new on-demand transportation service allows users to request rides from the online service providers at the touch of their fingers. Despite such great convenience, existing ORH systems require the users to expose their locations when requesting rides - a severe privacy issue in the face of untrusted or compromised service providers. In this paper, we propose a private yet efficient ride request scheme, allowing the user to enjoy public ORH service without sacrificing privacy. Unlike previous works, we consider a more practical setting where the information about the drivers and road networks is public. This poses an open challenge to achieve strong security and high efficiency for the secure ORH service. Our main leverage in addressing this problem is hardware-enforced Trusted Execution Environment, in particular Intel SGX enclave. However, the use of secure enclave does not lead to an immediate solution due to the hardware's inherent resource constraint and security limitation. To tackle the limited enclave space, we first design an efficient ride-matching algorithm utilizing hub-based labeling technique, which avoids loading massive road network data into enclave during online processing. To defend against side-channel attacks, we take the next step to make the ride-matching algorithm data-oblivious, by augmenting it with oblivious label access and oblivious distance computation. The proposed solution provides high efficiency of real-time response and strong security guarantee of data-obliviousness. We implement a prototype system of the proposed scheme and thoroughly evaluate it from both theoretical and experimental aspects. The results show that the proposed scheme permits accurate and real-time ride-matching with provable security.

An online ride-hailing (ORH) service, such as Uber and Didi Chuxing, can provide on-demand transportation service to users via mobile phones, which brings great convenience to people's daily lives. Along with the convenience, high privacy concerns are also raised when using an ORH service since users and drivers must share their real-time locations with the ORH server, which results in the leakage of the mobility patterns and additional privacy of users and drivers. In this paper, we propose a privacy-preserving ride-matching scheme, called pRide, for ORH service. pRide allows an ORH server to efficiently match rider and drivers based on their distances in the road network without revealing the location privacy of riders and drivers. Specifically, we make use of the road network embedding technique together with cryptographic primitives and design a scheme to securely and efficiently estimate the shortest distances between riders and drivers in road networks approximately. Moreover, by incorporating garbled circuits, the proposed scheme is able to output the nearest driver around a rider. We implement the scheme and evaluate it on the representative real-world datasets. The theoretical analysis and experimental results demonstrate that pRide achieves an efficient, secure, and yet accurate ride matching for ORH service.

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

Traditional mobile crowdsensing photo selection process focuses on selecting photos from participants to a server. The server may contain tons of photos for a certain area. A new problem is how to select a set of photos from the server to a smartphone user when the user requests to view an area (e.g., a hot spot). The challenge of the new problem is that the photo set should attain both photo coverage and view quality (e.g., with clear Points of Interest). However, contributions of these geo-tagged photos could be uncertain for a target area due to unavailable information of photo shooting direction and no reference photos. In this paper, we propose a novel and generic server-to-requester photo selection approach. Our approach leverages a utility measure to quantify the contribution of a photo set, where photos' spatial distribution and visual correlation are jointly exploited to evaluate their performance on photo coverage and view quality. Finding the photo set with the maximum utility is proven to be NP-hard. We then propose an approximation algorithm based on a greedy strategy with rigorous theoretical analysis. The effectiveness of our approach is demonstrated with real-world datasets. The results show that the proposal outperforms other approaches with much higher photo coverage and better view quality.

A mobile crowdsensing (MCS) platform motivates to employ participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. Recently, the appearance of data reconstruction method makes it possible to improve the platform's profit with a limited amount of sensing results in Compressive MCS (CMCS). However, It is of great challenge to the maximal profit for the CMCS platform, since it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In response to such challenges, we propose two profit-driven online participant selection mechanisms for the given task model and participant model. In ProSC, the sub-profit in each slot is maximized during the sensing period of a task, by combing a statistical-based quality prediction method and a repetitive cross-validation algorithm. In ProSC+, we jointly optimize the number of required participants and their spatial distribution to further improve the converging property. Finally, we conduct comprehensive evaluations, the results indicate the effectiveness and efficiency of our mechanisms.

WiFi networks are vulnerable to rogue AP attacks in which an attacker sets up an imposter AP to lure mobile users to connect. The attacker can eavesdrop on the communication, severely threatening users' privacy. Existing rogue AP detection solutions are confined to some specific attack scenarios (e.g., by relaying the traffic to a target AP) or require additional hardware. In this paper, we propose a crowdsensing based approach, named CRAD, to detect rogue APs in camouflage without specialized hardware requirement. CRAD exploits the spatial correlation of RSS to identify a potential imposter, which should be at a different location from the legitimate one. The RSS measurements collected from the crowd facilitate a robust profile and minimize the inaccuracy effect of a single RSS value. As a result, CRAD can filter out abnormal samples sensed in the realtime by dynamically matching the profile. We evaluate our approach with both a public dataset and a real prototype. The results show that CRAD can yield 90% detection accuracy and precision with proper crowd presence, even when the rogue AP is launched close to the legitimate one (e.g., within 1m).

With cloud services, users can easily host their data in the cloud and retrieve the part needed by search. Searchable encryption is proposed to conduct such process in a privacy-preserving way, which allows a cloud server to perform search over the encrypted data in the cloud according to the search token submitted by the user. However, existing works mainly focus on textual data and merely take numerical spatial data into account. Especially, geometric range search is an important queries on spatial data and has wide applications in machine learning, location-based services(LBS), computer-aided design(CAD), and computational geometry. In this paper, we proposed an efficient and generalized symmetric-key geometric range search scheme on encrypted spatial data in the cloud, which supports queries with different range shapes and dimensions. To provide secure and efficient search, we extend the secure kNN computation with dynamic geometric transformation, which dynamically transforms the points in the dataset and the queried geometric range simultaneously. Besides, we further extend the proposed scheme to support sub-linear search efficiency through novel usage of tree structures. We also present extensive experiments to evaluate the proposed schemes on a real-world dataset. The results show that the proposed schemes are efficient over encrypted datasets and secure against the curious cloud servers.

To protect the privacy of cloud data, searchable encryption is proved to be an important technique since it enables cloud users to search on encrypted data. Existing solutions for searchable encryption mainly focus on some basic search functions such as boolean search, similarity search or limited wildcard-based search. They cannot properly support the advanced search type: structured queries with generalized pattern matching (such as SQL-like queries) which is widely used for information retrieval in cloud database. In this paper, we propose a new searchable encryption scheme that realizes Structured queries with generalized Pattern matching to Search over Encrypted cloud data (SPSE). In particular, SPSE allows users to conduct generalized pattern matching queries on textual attribute values of structured data, and joint them with logical operators (AND, OR, NOT) to search over multiple attributes of the data sets. Besides the improvement of search functionalities, SPSE enhances the privacy by introducing two-tier encryption structure for data confidentiality and by hiding the search pattern of attribute fields to resist statistic analysis from untrusted parties. Security analysis proves that SPSE is KPA-secure. Experiments over real data sets show that SPSE achieves high search accuracy and practical search efficiency.

Solving large-scale systems of linear equations (L-SLE) is a common scientific and engineering computational task. But such problem involves enormous computing resources, which is burdensome for the resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing LSLE to the cloud brings great security concerns and challenges since the LSLE usually contains sensitive information. Previous works for secure outsourcing LSLE are mainly based on iterative methods which cause heavy computation cost for the client side. And they usually neglect to protect the number and position privacy of zero elements in the coefficient matrix, which is not secure enough for many applications. In this paper, with a series of disguise-based techniques, we propose a new efficient and non-iterative algorithm for securely outsourcing LSLE. Our algorithm only requires two rounds of communication between the client and cloud. Furthermore, the number and positions of zero elements in coefficient matrix can be hidden from the cloud with low computational complexity. Finally, we provide extensive theoretical analysis and experimental evaluation to show its high-efficiency and security compared to the previous works.

Combining with software-defined networking and IT virtualization technologies, Network Function Virtualization (NFV) has been proposed as an important technology to speed up deployment of new network services. VNF (Virtual Network Function) migration is a critical step to redeploy virtual network functions for providing better network services. Previous work in virtual network function migration primarily focused on the migration mechanism, including maintaining internal state consistency and reducing migrating time. In this paper, we address the problem of optimally migrating virtual network functions. As the computing and network resource requirement of virtual network functions have been changed, these virtual network functions have to be migrated to meet the computing and network resource constraints. As the SDN controllers conducted technology is used to migrate the virtual network functions, the migration cost depends on the buffer size of controllers and the time of transferring the internal state of virtual network functions. A cost model is proposed to evaluate the migration cost. The problem of optimal virtual network functions migration with satisfying computing and network resource constraints is NP-hard. A heuristic algorithm is proposed for computing the approximate solution. The effectiveness of the algorithms is validated by simulations evaluation.

Mobile crowdsensing (MCS) is a new paradigm which takes advantage of pervasive mobile devices to collaboratively collect data and analyze physical phenomenon. As mobile devices are owned and controlled by individuals with various capabilities and intentions, a main challenge MCS applications face is to ensure the credibility of the crowd contributed data. Existed works attempt to increase confidence level of the sensory measurements by validating the location. However, the required infrastructure or neighbor support may not always be available, and the unreliable form containing false sensory data with a valid location is implicitly ignored. In this paper, we propose a novel Crowd-based Credibility Improving Scheme (CCIS) to improve the credibility of data in possible false forms leveraging crowd data property and crowd participants' reputation. Based on the data clusters generated using a lightweight fixed-width clustering algorithm, CCIS is able to adequately identify and filter out the clusters constituted mainly by false data using reputation information as the classifier. We conduct simulations on a publicly available trace with crowd contributed temperature measurements, the results show that CCIS yields an improvement of overall data credibility of around 1.2 with clustering accuracy over 96%.

Searchable encryption is an important and challenging issue. It allows people to search on encrypted data. This is a very useful function when more and more people choose to host their data in the cloud and the cloud server is not fully trustable. Existing solutions for searchable encryption are only limited to some simple functions of search, such as boolean search or similarity search. In this paper, we propose a scheme for Generalized Pattern-matching String-search on Encrypted data (GPSE) in cloud systems. GPSE allows users to specify their search queries by using generalized wildcard-based string patterns (such as SQL-like patterns). It gives users great expressive power in specifying highly targeted search queries. In the framework of GPSE, we particularly implemented two most commonly used pattern matching search functions on encrypted data, the substring matching and the longest-prefix-first matching. We also prove that GPSE is secure under the known-plaintext model. Experiments over real data sets show that GPSE achieves high search accuracy.

Demand for Internet access from moving vehicles has been rapidly growing. Meanwhile, the overloading issue of cellular networks is escalating due to mobile data explosion. Thus, WiFi networks are considered as a promising technology to offload cellular networks. However, there pose many challenging problems in highly dynamic vehicular environments for WiFi networks. For example, connections can be easily disrupted by frequent handoffs between access points (APs). A scheme, called SWIMMING, is proposed to support seamless and efficient WiFi-based Internet access for moving vehicles. In uplink, SWIMMING operates in a “group unicast” manner. All APs are configured with the same MAC and IP addresses, so that packets sent from a client can be received by multiple APs within its transmission range. Unlike broadcast or monitor mode, group unicast exploits the diversity of multiple APs, while keeping all the advantages of unicast. To avoid possible collisions of ACKs from different APs, the conventional ACK decoding mechanism is enhanced with an ACK detection function. In downlink, a packet destined for a client is first pushed to a group of APs through multicast. This AP group is maintained dynamically to follow the moving client. The packet is then fetched by the client. With the above innovative design, SWIMMING achieves seamless roaming with reliable link, high throughput, and low packet loss. Testbed implementation and experiments are conducted to validate the effectiveness of the ACK detection function. Extensive simulations are carried out to evaluate the performance of SWIMMING. Experimental results show that SWIMMING outperforms existing schemes remarkably.

Network virtualization is a promising technology to enable multiple architectures to run on a single network. However, virtualization also introduces additional security vulnerabilities that may be exploited by attackers. It is necessary to ensure that the security requirements of virtual networks are met by the physical substrate, which however has not received much attention thus far. This paper represents an early attempt to consider the security issue in virtual network embedding, the process of mapping virtual networks onto physical nodes and links. We model the security demands of virtual networks by proposing a simple taxonomy of abstractions, which is enough to meet the variations of security requirements. Based on the abstraction, we formulate security-aware virtual network embedding as an optimization problem, proposing objective functions and mathematical constraints which involve both resource and security restrictions. Then a heuristic algorithm is developed to solve this problem. Our simulation results indicate its high efficiency and effectiveness.

IEEE 802.22 wireless regional area network (WRAN) standard employing cognitive radios is gaining much attention recently. The WRAN standard targets reuse of unused TV channels. We propose a device-to-device wireless regional area network (D2DWRAN) to extend the capacity of IEEE 802.22. Network capacity can be increased by supporting direct intra-cell device to device (D2D) communication through channel reuse and also with aggregation of nonadjacent multiple operating channels. Self-coexistence of neighboring IEEE 802.22 cells is a major challenge in WRANs, since the availability of channels varies frequently and channels are reused by every cell as much as possible. Currently, IEEE 802.22 does not consider D2D communication. We propose a two-tier spectrum sharing mechanism for channel allocation in D2DWRANs - both at intra-cell and inter-cell levels. Algorithms and a thorough analysis are presented. We examine our proposal through simulations. Results show significant performance improvement compared to IEEE 802.22. However, there are many hurdles to cross, such as, coexistence with other cognitive radio networks (CRNs), the intra-cell routing, allocation of other network resources, etc. We believe that further work in this domain can lead to increase in capacity not only in WRANs but also in other cellular networks.

Unmanned Aerial Vehicles (UAVs) have emerged as promising relay platforms to improve the connectivity of ground Mobile Ad Hoc Networks (MANETs). Due to the relatively high cost of UAVs, a lot of efforts have been made to optimize the deployment of UAVs so that the number of UAVs needed in maintaining the connectivity of ground nodes can be minimized. However, existing work on optimization of UAVs' deployment hasn't considered the situation that there are already some UAVs deployed in the field. In this paper, we study the problem of deploying minimum number of UAVs to maintain the connectivity of ground MANETs under the condition that some UAVs have already been deployed in the field. We formulate this problem as a Minimum Steiner Tree problem with Existing Mobile Steiner points under Edge Length Bound constraints (MST-EMSELB) and prove that the problem is NP-Complete. We also propose an Existing UAVs Aware (EUA) polynomial time approximate algorithm for the MST-EMSELB problem that uses a maximum match heuristic to compute new positions for existing UAVs. Simulation results demonstrate that the proposed EUA method has bester performance than a non-EUA method in the term of needed new UAV numbers. Compared with the non-EUA method, the EUA method can reduce at most 60% of the new UAVs number.

Flexible channelization can enhance spectrum efficiency by adapting channel bandwidth and center frequency compared with traditional fixed width channel. The key challenge of this new mechanism lies in how to determine proper channel width for each link in a distributed manner without coordination. In this work, we investigate the spectrum sharing problem with flexible channelization from game-theoretic perspective, in which the nodes are rational and pursue their own objects through selecting proper channel width and frequency. Compared with current related works, the proposed game can convergence to an efficient Nash equilibrium without any exogenous factor such as charging or incentive scheme that influence nodes' behavior. We cast the game to a potential game by exploiting the structure of payoff function and propose distributed algorithm to achieve Nash equilibrium.

Fuzzy keyword search is an important and necessary functionality for information retrieval in modern cloud storage services, since cloud users may submit queries with typos errors or have deficient knowledge about the underlying keywords of cloud data sets. However, for the purpose of privacy preservation, data is usually encrypted before outsourcing to the cloud, which greatly compromises the data utilization flexibility and efficiency. In this paper, we propose F2SE as a novel fuzzy keyword search scheme over encrypted cloud data. Using keyword fingerprint extraction and secure kNN encryption, F2SE can achieve a top-k ranked fuzzy keyword search according to the keyword similarity. Meanwhile, F2SE can return keywords containing special sub strings customized by cloud users with deficient background knowledge, which can be used for exploratory search or uncertain search. Thorough security analysis shows F2SE is KPA-secure while extensive experiments over real data sets demonstrate that F2SE has a low memory overhead and practical searching time cost.

Urban traffic congestions have become a financial and societal burden in many cities. Efficient traffic management solutions mitigating such congestions require a reliable modeling and estimation of traffic jams. In urban traffic, the modeling challenges are related to flow collisions and gridlocks created at intersections. In this paper, we propose a traffic light extended Cell Transmission Model (CTM), where the influence of flow collisions and gridlocks are modeled by a single CK parameter. Our approach only requires adapting CK for each intersection type/geometry instead of a complex mathematical formulae proposed in related works. We formalize the description of our urban CTM, and evaluate its capability to model traffic volumes and jams against the microscopic traffic simulator SUMO (Simulation of Urban MObility). Results show that the CK parameter is able to closely reproduce the impact of collisions and gridlocks on traffic jam, making the proposed urban CTM suitable to predict traffic congestions in urban environments.

Enterprise WLAN consists of many APs connected to wired backbone network. Conventional DCF mechanism can not completely prevent conflicts between APs which cause large quantities of transmission failure and retransmission overhead. However, to keep compatible with 802.11-compliant clients, it is difficult to discard DCF. In this paper, we proposed a framework called DPS, which incorporates centralized scheduling with DCF. In DPS, downlink packets are not forwarded as soon as they arrived at APs. Instead, a central controller schedules their forwarding. APs still use DCF to send downlink packets according to the controller's instructions. DPS preserves DCF and requires no modifications to 802.11-compliant clients. Under DPS framework, we proposed a scheduling algorithm, ensuring downlink packets be sent with high success rate. In this way, we can significantly reduce transmission failure and retransmission overhead. Experiment results indicated that DPS can significantly improve the performance of enterprise WLAN compared with DCF without any scheduling.

Successive interference cancellation (SIC) is an effective way of multipacket reception to combat interference in wireless networks. We focus on link scheduling in wireless networks with SIC, and propose a layered protocol model and a layered physical model to characterize the impact of SIC. In both the interference models, we show that several existing scheduling schemes achieve the same order of approximation ratios, independent of whether or not SIC is available. Moreover, the capacity order in a network with SIC is the same as that without SIC. We then examine the impact of SIC from first principles. In both chain and cell topologies, SIC does improve the throughput with a gain between 20 and 100 percent. However, unless SIC is properly characterized, any scheduling scheme cannot effectively utilize the new transmission opportunities. The results indicate the challenge of designing an SIC-aware scheduling scheme, and suggest that the approximation ratio is insufficient to measure the scheduling performance when SIC is available.

The association policy in current IEEE 802.11 networks usually considers Received Signal Strength Indication (RSSI) to be the only metric to capture access link quality. However, when a Mesh Client (MC) in IEEE 802.11-based Wireless Mesh Network (WMN) needs to be associated with the most appropriate Mesh Access Point (MAP), the quality of both the access link and the routing path in mesh backhaul should be considered. To take into account this requirement, most existing approaches rely on MAPs to provide more information for MCs such as traffic load, routing metric, airtime cost, etc. These solutions inevitably need to modify the IEEE 802.11 standard or wireless interface drivers on both MAPs and MCs, which is not feasible or flexible in actual deployment. In this paper, a network-leading association scheme is proposed for IEEE 802.11 WMNs. It is completely operated by MAPs and does not require any modification on MCs. It also adapts to the dynamic network environment and always selects the best MAP to serve an MC. Simulation results on ns-3 platform indicate that the network-leading association scheme remarkably improves the performance of IEEE 802.11 WMNs as compared with the existing approaches.

Providing Internet access on buses allow people to read news, check emails, watch sport games, and hence can greatly improve the quality of people's life. This paper proposes an adaptive routing protocol, called R-BUS for a bus network. R-BUS selects a route based on the link's lifetime and its communication signal quality. The protocol takes the bus mobility and the radio propagation model into account. A real map scenario is employed to evaluate the performance through simulation. The simulations show that R-BUS can guarantee the communication quality and also increase the route reliability. Compared to the existing routing protocols, R-BUS can achieve higher packet delivery ratio, lower average packet delay and lower control overhead.

Data caching is an important technique in wireless ad hoc networks, where it can increase data availability and significantly improve the efficiency of information access by reducing the access latency and bandwidth usage. However, designing efficient caching algorithms is non-trivial when network nodes have limited memory. After analyzing the benefit function of DGA (distributed greedy algorithm) (by Tang), an improved DGA algorithm (IDGA) is proposed and the factor of data access frequency is investigated to get better performance in this paper. The data access frequency is divided into two parts, which are local access frequency and other node access frequency, and then different weights are assigned to them respectively. Thus, data caching can be implemented based on different network application. The simulation results using the network simulator NS2 show that, compared with DGA, our algorithm is more applicable, and different benefit functions can be chosen for various applications to get lower latency and higher availability.

We present a novel spectrum access scheme for open spectrum networks. Different from existing works, this work considers the scenario that contending secondary users have heterogeneous channel availability. It is proved to be NP hard to find the optimal spectrum assignment in this scenario. To solve this problem, a novel approximation algorithm is proposed which is based on the maximum weighted matching technique. The performance of the algorithm is evaluated through extensive simulations. Compared with the general optimal spectrum allocation scheme without considering the difference of spectrum availability, the experiments' results demonstrate that the new matching based algorithm improves the network throughput significantly, generally by 20% to 60%. The algorithm's computation complexity is also at a low level of 0(M*n2*m).

The architecture and characteristic of wireless mesh networks (WMN), as well as the significance of intrusion detection system (IDS) in its application were investigated. Based on the embedded analyzing of IDS technologies in ad hoc network and WLAN (wireless local area network), combined with the security requirements in WMN itself, it is concluded that both the architecture of distributed and cooperative IDS in ad hoc network and distributed IDS in WLAN can not be applied directly in WMN. A new architecture of asymmetric distributed and cooperative IDS for WMN based on the application of agent was proposed in the paper. This new architecture of IDS in WMN was then simulated by the attack detection of IEEE 802.11 MAC selfish behavior in NS2, using the IDS selfish behavior attack detection model ground on the judgment mechanism of double mode, which was also brought forward. The result of the simulation shows that the given new architecture of asymmetric distributed and cooperative IDS and the IDS selfish behavior attack detection model fitted WMN well.

In wireless sensor networks, energy is the most crucial resource. In-network data processing is a common technique in which an intermediate proxy node is chosen to house a possibly complicated data transformation function to consolidate the sensor data streams from the source nodes, en route to the sink node. We investigate into the placement problem of the proxy. We formulate and solve the energy minimization problem analytically, based on an ENergy- Efficient Rate-Governed Yardstick (ENERGY). An optimal solution is derived based on complete network topology information. Taking into account realistic sensor network constraints that only neighboring network connectivity is known to a node, we develop an approximate but effective solution, ENERGY . We evaluate the performance of ENERGY, which performs well even in low-density networks and for queries requesting from data sources at a distance.