Data marketing serves as a centralized third party, aggregating data owners and trading their data with buyers to promote data circulation. However, the security and privacy of marketing rely on a trusted central authority, single points of failure and rights abuse can easily cause untrusted marketing and data leakage. In this paper, we propose a federated authority management for attribute-based encryption (ABE) scheme in blockchain-assisted data marketing. First, we design an authority blockchain with a threshold issuance protocol to replace the trusted authority in ABE, which achieves trusted authorization for marketing and eliminates the single points of failure. Second, we design an on/off-chain phased right revocation method, which efficiently revokes a buyer's access rights by invoking smart contracts to update the on-chain rights status, and without requiring off-chain re-authorization. Thus, this method prevents insecure marketing due to the rights abuse. Theoretical analysis shows that our scheme could protect marketing security and data privacy. Experimental results confirm that it reduces the access and revocation cost to a constant level and decreases the storage overhead of marketing by n times compared to existing schemes.

In the Internet of Vehicles (IoVs), certificateless-based authentication has emerged as a promising approach to secure federated learning (FL) participation. However, existing certificateless-based authentication schemes still exhibit three notable limitations: (i) some schemes cannot provide security against public-key replacement (PKR) attacks and key-leakage threats; (ii) most existing schemes do not take realistic coalition attacks into account; (iii) while some schemes applicable to FL settings can offer relatively comprehensive security guarantees, they often rely on costly cryptographic operations. These limitations make it challenging for certificateless authentication frameworks to simultaneously ensure high security and low latency, particularly in resource-constrained FL deployments within IoV environments. To address these challenges, we propose FedAuth, a certificateless, lightweight, and privacy-enhanced authentication scheme tailored for FL in IoVs. First, FedAuth establishes a structured pseudonymization mechanism that enables vehicles to anonymously participate in the FL process while maintaining accountability. Afterwards, FedAuth ensures robust authentication security by cryptographically anchoring key generation to each vehicle’s pseudonym, enforcing strict key validation, and introducing hash-bound multi-layered signature verification that prevents signature recombination, thereby resisting PKR attacks, coalition attacks, and key-leakage threats. Second, FedAuth guarantees the authenticity and integrity of gradients through lightweight signature construction, which supports batch authentication while avoiding expensive operations such as bilinear pairings, thereby reducing latency. Security analysis shows that FedAuth preserves identity privacy and ensures model integrity in the face of adversarial conditions. Experimental results confirm that FedAuth significantly reduces computational overhead by at least 43.42% and saves not less than 19.8 KB of communication cost per 100 vehicles, outperforming state-of-the-art schemes in both efficiency and robustness.

In vehicular networks, pseudonyms are a fundamental mechanism for achieving anonymous communication. However, existing Authentication and Key Agreement (AKA) schemes typically require each pseudonym to be cryptographically bound to private information, such as a certificate or a secret key, to ensure authenticity. This approach leads to significant challenges, including complex certificate management and the inherent risks of secret key escrow. Furthermore, to maintain unlinkability, vehicles must store a large pool of pseudonyms and their associated private information–leading to prohibitive storage costs. The high computation and communication costs of such schemes are also ill-suited for the delay-sensitive nature of vehicular environments. To address these limitations, we propose a pseudonym-only approach for anonymous AKA in vehicular networks. The primary contribution of our scheme is its novel "pseudonym-only" approach to authentication, without the need to combine it with other private information. PidTree provides an efficient pseudonym generation method. Our scheme involves lightweight computation operations such as hash functions and Lagrange interpolation. The security analysis shows that our scheme satisfies the essential security and privacy requirements of vehicular networks. Our scheme reduces the storage cost for the trusted party fromO(MN)toO(M)and the storage cost for a vehicle by at least 86.50%. The performance analysis also shows that our scheme outperforms the representative schemes in terms of computation cost and simulation results.

Collusion attacks, where the server and malicious clients collaborate to bypass gradient source confirmation or tamper with aggregation results, cause a fundamental damage to the training process in federated learning (FL). However, existing verifiable FL frameworks typically adopt a split-verification model-clients can independently validate the correctness of aggregation results, while the server is responsible for confirming the legitimacy of gradient sources. Thus, the collusion attack has emerged as a critical and intractable vulnerability, as it completely collapses this split-verification model, thereby invalidating such verification mechanisms. To tackle this fundamental issue, we propose an innovative dual-verifiable FL framework. Specifically, by leveraging vector commitments, our scheme first integrates both gradient source confirmation and aggregation result verification into a unified framework. Based on this unified design, our scheme implements two targeted strategies to defend against collusion attacks. To prevent collusion-enabled gradient source spoofing, our scheme introduces a semi-trusted verification cluster in place of unreliable server-side validation and embeds an anonymized identity-check strategy to collaboratively confirm gradient source legitimacy. To counter collusion-driven manipulation of gradient aggregation results, our scheme customizes auxiliary verification proofs with a computational one-wayness for client-uploaded gradients. This renders it infeasible for adversaries to tamper with the aggregation result through reverse engineering. Under experiments and security analyses, our scheme achieves reliable dual-verification and robust resistance to collusion attacks. Moreover, it reduces computation and communication overhead by at least 40.83% and 50.47%, respectively, compared to state-of-the-art verifiable FL schemes.

Federated Learning (FL) has recently gained prominence in the context of Vehicular Ad-hoc Networks (VANETs) as a promising approach to enhancing autonomous driving capabilities. However, vehicles' high mobility, real-time communication, and dynamic network topology lead to frequent disconnections during operation, which may slow down the convergence of FL or even lead to training failure. In this study, we propose a scalable aggregation scheme (SAPE) designed to improve computation efficiency and address vehicle dropouts. SAPE employs a lossless encoding algorithm with parallel technology for efficient aggregation of large vectors. Then, we leverage TJL (ACSAC '22) to reconstruct gradients for dropout vehicles, using online vehicles to establish a$k$-regular graph. In a network with$N$vehicles, SAPE achieves a secure aggregation overhead of$O(log^{2}(N))$, as opposed to$O(N^{2})$, tolerating a vehicle dropout rate of up to 33%. Furthermore, we conduct a theoretical security analysis of SAPE to prove its security under honest-but-curious (HBC) and malicious attack models. Extensive experiments show that SAPE outperforms existing baseline aggregation schemes by up to 1.4× speedups in aggregation time.

Existing video captioning methods merely provide shallow or simplistic representations of object behaviors, resulting in superficial and ambiguous descriptions. However, object behavior is dynamic and complex. To comprehensively capture the essence of object behavior, we propose a dynamic action semantic-aware graph transformer. Firstly, a multi-scale temporal modeling module is designed to flexibly learn long and short-term latent action features. It not only acquires latent action features across time scales, but also considers local latent action details, enhancing the coherence and sensitiveness of latent action representations. Secondly, a visual-action semantic aware module is proposed to adaptively capture semantic representations related to object behavior, enhancing the richness and accurateness of action representations. By harnessing the collaborative efforts of these two modules, we can acquire rich behavior representations to generate human-like natural descriptions. Finally, this rich behavior representations and object representations are used to construct a temporal objects-action graph, which is fed into the graph transformer to model the complex temporal dependencies between objects and actions. To avoid adding complexity in the inference phase, the behavioral knowledge of objects is distilled into a simple network through knowledge distillation. The experimental results on MSVD and MSR-VTT datasets demonstrate that the proposed method achieves significant performance improvements across multiple metrics.

Accurate diagnosis of malignant lung nodules in Computed Tomography (CT) images is crucial for reducing patient mortality. However, deep learning-based approaches for lung nodule diagnosis often struggle to achieve high accuracy due to the interference of redundant noise from tissue slices and insufficient exploration of the interactions between cross-modal attribute data, such as spiculation, lobulation, and calcification. To overcome these obstacles, we propose a novel Multi-perspective Information Enhancement Graph Attention Network (MIE-GAT) for automated lung nodule diagnosis. Unlike existing methods, our approach integrates slice-based image retrieval with cross-modal attribute interaction knowledge to effectively reduce redundant noise from tissue slices and capture the intricate relationships between various nodule attributes, significantly improving diagnostic accuracy. MIE-GAT consists of two subgraphs: a slice subgraph for spatial relationships within CT slices, and an attribute subgraph for nodule attribute interactions. The model introduces multiple central nodes, each focusing on different perspectives of the 3D CT scan. By facilitating directed information propagation between central nodes, MIE-GAT enhances feature representations across multiple scales, progressively improving its ability to distinguish subtle features of the nodule. Ultimately, this multi-perspective information enhancement mechanism enables the model to align attribute-based interaction knowledge and perform effective slice-based retrieval, focusing on the most relevant slices while reducing the interference of redundant noise. Extensive experiments on the LIDC-IDRI and LIDP datasets demonstrate that MIE-GAT outperforms state-of-the-art methods, confirming its effectiveness in improving the accuracy of automated lung nodule diagnosis. The source code for MIE-GAT is available in the anonymous repository at https://github.com/MindExpanse/NoduleClassification.

Source) code summarization is the task of automatically generating natural language summaries (also called comments) for given code snippets. Recently, with the successful application of large language models (LLMs) in numerous fields, software engineering researchers have also attempted to adapt LLMs to solve code summarization tasks. The main adaptation schemes include instruction prompting, taskoriented (full-parameter) fine-tuning, and parameter-efficient fine-tuning (PEFT). However, instruction prompting involves designing crafted prompts and requires users to have professional domain knowledge, while task-oriented fine-tuning requires high training costs, and effective, tailored PEFT methods for code summarization are still lacking. In this paper, we propose an effective prompt learning framework for code summarization called PromptCS. It no longer requires users to rack their brains to design effective prompts. Instead, PromptCS trains a prompt agent that can generate continuous prompts to unleash the potential for LLMs in code summarization. Compared to the human-written discrete prompt, the continuous prompts are produced under the guidance of LLMs and are therefore easier to understand by LLMs. PromptCS is non-invasive to LLMs and freezes the parameters of LLMs when training the prompt agent, which can greatly reduce the requirements for training resources. We evaluate the effectiveness of PromptCS on the CodeSearchNet dataset. Experimental results show that PromptCS significantly outperforms instruction prompting schemes (including zero-shot learning and few-shot learning) on all four widely used metrics, including BLEU, METEOR, ROUGE-L, and SentenceBERT, and is comparable to the task-oriented fine-tuning scheme. In some base LLMs, e.g., CodeGen-Multi-2B and StarCoderBase-1B and -3B, PromptCS even outperforms the task-oriented fine-tuning scheme. More importantly, the training efficiency of PromptCS is faster than the task-oriented fine-tuning scheme, with a more pronounced advantage on larger LLMs. The results of the human evaluation demonstrate that PromptCS can generate more good summaries compared to baselines.

In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

As security concerns in blockchain platforms continue to rise, triggered by substantial financial losses, detecting vulnerabilities in smart contracts has emerged as a crucial focus for both academia and industry. Although many promising vulnerability detection methods for Ethereum have been proposed in recent years, their long-term reliability and adaptability across different versions of smart contracts remain unresolved. Specifically, the performance of these methods tends to degrade over time, and in some cases, they may even fail entirely. A key factor contributing to this dilemma is the regular updates of Solidity versions. These updates often introduce new features or syntax changes, which significantly influence how vulnerabilities are manifested and detected. To tackle this challenge, we propose Triplet Detection (TD), a triplet learning-based vulnerabilities detection framework, to preserve invariant vulnerability knowledge across multiple Solidity versions. In TD, we propose a novel Smart Offline Mining (SOM) strategy to guide triplet selection, ensuring the learned embedding capture both foundational and version-independent vulnerability features. The experimental results demonstrate that TD outperforms state-of-the-art vulnerability detection tools and baseline methods. Furthermore, TD achieves superior performance in detecting vulnerabilities across different versions of smart contracts (e.g., from v0.4 to v0.8), highlighting its capability to tackle the challenges of long-term reliability and adaptability in detection models due to the updates of smart contract versions.

In enterprize-scale Internet of Things, users need to query data by accessing the resource-constrained smart nodes. Such queries typically include data from one node (DON), and data from one catalog of multiple nodes (DOC). Traditional access control mechanisms often prove inadequate due to the lack of efficient policy management. Their authorization time for queries is linear with respect to the number of access control rules in the policy, which greatly impedes access granularity, efficiency, and scale. To address this issue, we propose FECAC, a fine-grained and efficient capability-based access control mechanism for DON and DOC access queries. Specifically, FECAC builds a policy matching tree structure by translating the rule into matching properties in the tree node, which avoids authorizing queries by traversing the entire rule collection. We then introduce an authorization scheme to match the elements of access requests in a top-down manner, and check the rules with the internal properties of the data queries sublinearly, which efficiently combines the requests of DOC and DON. Further, we give a concrete operation of FECAC from query authorization scheme to execute data querying based on capabilities. Finally, we demonstrate the improved and more stable evaluation efficiency of FECAC compared to existing schemes.

With the popularity of location-based services (LBSs) and the explosive growth of spatial data, massive spatial data has been outsourced to cloud servers for storage and query services, such as spatial range and fuzzy keyword query. To protect data and user privacy, it is essential to encrypt the data and query requests before uploading them. However, encrypting the data makes search on ciphertext more challenging. Moreover, the existing schemes do not support spatial range and fuzzy keyword query (SFKQ for short) simultaneously. To this end, we first leverage the indecomposable property of primes and the Geohash algorithm to create an index vector and query trapdoor vector. We then propose concrete constructions for SFKQ, which support both spatial range query and fuzzy multikeyword query in a single interaction. Additionally, we carefully design a novel Geohash-based index structure (referred to as GeoTree) along with a pruning strategy to accelerate search efficiency. Finally, we provide the formal security analysis to demonstrate that the proposed SFKQ scheme is indistinguishable under chosen-plaintext attacks (IND-CPA), and we conduct comprehensive experiments to validate the accuracy and efficiency.

Blockchain database has been widely applied in various fields, providing a secure architecture for data storage and sharing. In blockchain databases, the use of sharding technology can enhance the system’s scalability and improve transactions in the network to be processed in parallel. However, when applying searchable encryption techniques to query encrypted data in sharding blockchains, frequent data access to different shards results in uneven load distribution, leading to hotspot issues and reducing system efficiency. To tackle the challenge, we presentSDB, a scheme integrating searchable encryption with sharding technology to enhance the scalability of blockchain databases. Firstly, we introduce a two-stage sharding mechanism. It performs pre-sharding based on keywords, and then implements fine-grained dynamic adjustments through improved jump consistent hashing, effectively resolving the load imbalance problem. Secondly, we present a cross-shard query strategy based on encrypted indexes, which constructs verifiable indexes for encrypted data and converts user queries into a multiway query tree. This addresses the cross-shard query optimization problem in encrypted environments. Under the experiment and security analysis,SDBachieves efficient cross-shard queries with higher query performance and throughput, at least 30% and 20% higher than state-of-the-art scalable blockchain database schemes.

Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lack of interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features thereby reducing the scales of features. The learning speed could be accelerated significantly because of the reduction of the feature size. The experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each is higher than 93% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerability is reduced by nearly 80% compared with its original.

Federated learning (FL) in Vehicular Ad-hoc Networks (VANETs) enables vehicles to collaboratively train machine learning models by aggregating local gradients without revealing the training data. To ensure no gradient is revealed during aggregation, proposals are using a secret sharing-based strategy. A major bottleneck for applying these proposals in VANETs is the overhead of model aggregation across high-mobility vehicles. Particularly, the communication overhead grows exponentially due to the dynamic of VANETs. In the paper, we propose Batch-Aggregate, an efficient aggregation scheme for FL coping with high mobility and unstable connections of VANETs. By encoding the linear encryption into a short group signature, we combine authentication into aggregation protocol. When a registered vehicle trains its local model and sends the masked gradients to the nearby Road-side Unit (RSU), the RSU can independently check the gradients for validity and aggregate the parameters in a batch way. Thus, the computation time of the aggregator will be reduced to$\mathcal {O}(n)$while the gradients can be aggregated in one communication round per training iteration. Moreover, our scheme provides privacy properties such as anonymity and unlinkability. The simulations show that the computation overhead of Batch-Aggregate grows linearly under the batch-enabled scheme, which reduces up to 50% over the existing schemes.

Internet of Vehicles (IoVs) is increasingly used as a medium to propagate critical information via establishing connections between entities such as vehicles and infrastructures. During message transmission, privacy-preserving authentication is considered the first line of defence against attackers and malicious information. To achieve a more secure and stable communication environment, ever-increasing numbers of blockchain-based authentication schemes are proposed. At first glance, existing approaches provide robust architectures and achieve transparent authentication. However, in these schemes, verifiers need to conduct real-time operations in the blockchain (e.g., querying certificates). To remedy this limit, we propose a privacy-preserving blockchain-based authentication protocol with global-updated commitment (PBAG). In PBAG, based on the issued certificates, a public global commitment is computed, and a unique evaluation proof is generated for each authorized vehicle. Instead of querying the blockchain in real-time, verifiers can independently authenticate vehicles using the global commitment that is pre-updated with the assistance of the blockchain. Moreover, our scheme proposes a dynamic update mechanism to ensure the freshness of the global commitment and evaluation proofs. Benefiting from the update mechanism, there will be an authentication failure for vehicles holding invalid certificates when using the latest global commitment, thus avoiding the time-consuming of checking the Certificate Revocation List (CRL). In terms of privacy protection, our scheme provides privacy properties such as anonymity and unlinkability. It allows anonymous authentication based on evaluation proofs and achieves traceability of identity in the event of a dispute. The simulation demonstrates that the average computation cost of verifying per message is 0.36ms under the batch-enabled mechanism, reducing by more than 63.7% compared with existing schemes.

Federated learning (FL) is vulnerable to data poisoning attacks when an adversary attempts to upload poison gradients with the intent to corrupt the global model of FL. Various approaches have been proposed to counter these risks. However, it becomes challenging when one tries to preserve the privacy of FL participants and ensure robustness against data poisoning attacks. In this paper, we propose DPFLA, a novel scheme that can detect poisoning attacks without revealing the actual gradients of participants. DPFLA is a lossless aggregation scheme delicately designed for adopting masks to protect private data while extracting poisoned data features. Specifically, we first apply removable masks to the gradients outputted by each participant. Second, we aggregate the masked data and decompose them using Singular Value Decomposition (SVD) to extract specific features as well as achieve dimensionality reduction. Third, we leverage a clustering paradigm to detect poison gradients from the low dimension and eliminate them in the following training rounds. We conducted extensive experiments to demonstrate that DPFLA can detect poison gradients effectively. Additionally, the comparisons of case studies demonstrate that DPFLA outperforms the state-of-the-art methods.

Real-time and interactive traffic information sharing systems are crucial in the Internet of Vehicles (IoV) as they enable vehicles to make informed decisions, thereby improving the efficiency of intelligent transportation systems (ITS). Message authentication ensures the accuracy, integrity, and tamper-resistance of information in IoV. Existing schemes aim to achieve time-critical message authentication . However, these schemes are time-consuming and cannot meet the real-time requirements of IoV. Additionally, there are issues with latency in data synchronization and data redundancy when vehicles traverse different domains. We propose an efficient, distributed, and resistant-to-malicious-attacks authentication scheme based on the reputation mechanism. Our scheme supports batch verification, enabling fast authentication. By leveraging the decentralized and ledger-synchronized features of blockchain , our distributed scheme reduces data redundancy. We also employ a reputation mechanism to ensure reliable reports in IoVs. We experimentally confirm that our scheme outperforms EADA (59.73%), RCoM (76.35%), MLGSDT (63.36%), and TRAJ (82.08%). This approach provides a secure and reliable solution for report authentication.

With the increasing requirements of industrial production efficiency, the Industrial Internet has played a very important role in the fourth industrial revolution. However, the current Industrial Internet still has many drawbacks, especially in terms of network systems, such as low network expansion, inconvenient troubleshooting, and low data transmission efficiency. For this motivation, a novel SRv6-based multidomain integrated architecture (SMA) for the Industrial Internet has been proposed. Multilayer controllers are deployed in the SMA, and a software-defined network controller that generates the transmission path is replaced by SMA nodes, which realizes the high network scalability and efficient data transmission of the Industrial Internet. The faulty node in the SMA can be quickly and accurately identified through the periodic detection actively sent by the controller node in the domain and the passive feedback of the SMA nodes, and the generated SMA node trusted set (SNTS) can be used for forwarding path generation. A Bellman–Ford algorithm with a hop count constraint based on the total number of SNTS nodes is proposed, which effectively avoids long-path forwarding and improves network resource utilization. Through theoretical analysis, the safety and scalability of the SMA have been fully verified. The simulation results of the SMA on the experimental platform show that the SMA is superior to the existing Industrial Internet network structure in terms of troubleshooting efficiency of faulty nodes, network throughput, and data communication overhead. In the Industrial Internet, when the proportion of SMA nodes reaches 30%, the SMA controller can control nearly 80% of the traffic. In addition, the maximum link utilization rate will be greatly reduced, which means better adjustment of network load balance.

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

The blockchain technology achieves security by sacrificing prohibitive storage and computation resources. However, in mobile systems, the mobile devices usually offer weak computation and storage resources. It prohibits the wide application of the blockchain technology. Edge computing appears with strong resources and inherent decentralization, which can provide a natural solution to overcoming the resource-insufficiency problem. However, applying edge computing directly can only relieve some storage and computation pressure. There are some other open problems, such as improving confirmation latency, throughput, and regulation. To this end, we propose an edge-computing-based lightweight blockchain framework (ECLB) for mobile systems. This paper introduces a novel set of ledger structures and designs a transaction consensus protocol to achieve superior performance. Moreover, considering the permissioned blockchain setting, we specifically utilize some cryptographic methods to design a pluggable transaction regulation module. Finally, our security analysis and performance evaluation show that ECLB can retain the security of Bitcoin-like blockchain and better performance of ledger storage cost in mobile devices, block mining computation cost, throughput, transaction confirmation latency, and transaction regulation cost.

Vehicular Ad-hoc Networks (VANETs) supporting the seamless operation of autonomous vehicles introduce various network-connected devices. The widespread devices are engaged in VANETs so that users can enjoy advantageous computing and reliable services. The combination brings in massive real-time message propagation and dissemination, which would be leveraged by the adversaries to perform data association, integration analysis and privacy mining. To address such challenges, existing authentication schemes use n pseudonym certificates for pre-defined k times and try to keep the vehicles anonymous. These schemes require fresh certificates for each authentication process, which cost more communication and storage resources. In this paper, we propose a novel privacy-preserving authentication protocol (P2BA) in bilinear groups, where a registered vehicle signs a traffic-related message and sends it to the nearby Road-side Unit (RSU) together with its blinded certificate. The RSU is able to independently check the message for validity based on a non-interactive zero-knowledge proof protocol. In this way, the computation time has been reduced fromO(n) toO(1) while the storage overhead fromO(nk) toO(n) compared to anonymous authentication protocols. Moreover, our scheme provides privacy properties such as anonymity and unlinkability. The simulations show that the message authentication can be processed by individual RSUs within 1 ms under the batch-enabled scheme, which outperforms the existing schemes in terms of computation overhead and latency.

Sybil attack can counterfeit traffic scenario by sending false messages with multiple identities, which often causes traffic jams and even leads to vehicular accidents in vehicular ad hoc network (VANET). It is very difficult to be defended and detected, especially when it is launched by some conspired attackers using their legitimate identities. In this paper, we propose an event based reputation system (EBRS), in which dynamic reputation and trusted value for each event are employed to suppress the spread of false messages. EBRS can detect Sybil attack with fabricated identities and stolen identities in the process of communication, it also defends against the conspired Sybil attack since each event has a unique reputation value and trusted value. Meanwhile, we keep the vehicle identity in privacy. Simulation results show that EBRS is able to defend and detect multi-source Sybil attacks with high performances.

In this paper, we introduce a new topic model to understand the chaotic microblogging environment by using hashtag graphs. Inferring topics on Twitter becomes a vital but challenging task in many important applications. The shortness and informality of tweets leads to extreme sparse vector representations with a large vocabulary. This makes the conventional topic models (e.g., latent Dirichlet allocation [1] and latent semantic analysis [2]) fail to learn high quality topic structures. Tweets are always showing up with rich user-generated hashtags. The hashtags make tweets semi-structured inside and semantically related to each other. Since hashtags are utilized as keywords in tweets to mark messages or to form conversations, they provide an additional path to connect semantically related words. In this paper, treating tweets as semi-structured texts, we propose a novel topic model, denoted as Hashtag Graphbased Topic Model (HGTM) to discover topics of tweets. By utilizing hashtag relation information in hashtag graphs, HGTM is able to discover word semantic relations even if words are not co-occurred within a specific tweet. With this method, HGTM successfully alleviates the sparsity problem. Our investigation illustrates that the user-contributed hashtags could serve as weakly-supervised information for topic modeling, and the relation between hashtags could reveal latent semantic relation between words. We evaluate the effectiveness of HGTM on tweet (hashtag) clustering and hashtag classification problems. Experiments on two real-world tweet data sets show that HGTM has strong capability to handle sparseness and noise problem in tweets. Furthermore, HGTM can discover more distinct and coherent topics than the state-of-the-art baselines.

Mining topics in Twitter is increasingly attracting more attention. However, the shortness and informality of tweets leads to extreme sparse vector representation with a large vocabulary, which makes the conventional topic models (e.g., Latent Dirichlet Allocation) often fail to achieve high quality underlying topics. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTM establishes word semantic relations, even if they haven't co-occurred within a specific tweet. In addition, we enhance the dependencies of both multiple words and hash tags via latent variables (topics) modeled by HGTM. We illustrate that the user-contributed hash tags could serve as weakly-supervised information for topic modeling, and hash tag relation could reveal the semantic relation between tweets. Experiments on a real-world twitter data set show that our model provides an effective solution to discover more distinct and coherent topics than the state-of-the-art baselines and has a strong ability to control sparseness and noise in tweets.

In this paper, an efficient method using various histogram-based (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories is presented. Principal component analysis (PCA) is used to project the original high dimensional histograms onto their eigenspaces. Lower dimensional eigenfeatures are then used to train support vector machines (SVMs) to classify images into their categories. Experimental results show that even though different descriptors perform differently, they are all highly redundant. It is shown that the dimensionality of all these descriptors, regardless of their performances, can be significantly reduced without affecting classification accuracy. Such scheme would be useful when it is used in an interactive setting for relevant feedback in content-based image retrieval, where low dimensional content descriptors enable fast online learning and reclassification of results.