With the rapid development of large language models, the fine-tuning of downstream tasks has become a key problem. As a new learning paradigm, prompt learning can greatly reduce the number of updated parameters and effectively reduce the communication cost in federated learning scenarios by freezing the pre-trained model and fine-tuning soft prompt parameters. However, for the federated prompt tuning, the local prompt usually contains the user's personal information, and the text input of the user are easily leaked through the aggregation directly on the server. Meanwhile, the soft prompt is vulnerable to poisoning attacks launched by malicious users (such as backdoor). For the above problems, this paper proposes a privacy-preserving federated prompt fine-tuning against poisoning attacks. Specifically, we use homomorphic encryption to ensure the confidentiality of local prompts, and aggregate prompts under ciphertext to generate global prompts. Secondly, the proposed mechanism of backdoor detection is used to judge whether the prompt contains backdoors to resist the malicious attacks in the form of ciphertext. Both local and global prompts are invisible to the server during the entire training process. Finally, theoretical analysis and experimental results show that the accuracy error between the plaintext domain and the ciphertext domain is controlled within 2%-5% under backdoor attack, which effectively enhances the robustness of the model.

Recent advances have highlighted the potential of diffusion models in Video Anomaly Detection (VAD). Diffusion models are typically employed to generate negative instances to distinguish them from positive ones. However, the existing diffusion model architectures, generally based on the reconstruction of low-level noisy features, introduce spurious correlations due to shortcut learning, which undermines the robustness of anomaly detection. In this work, we leverage normal-specific representations to guide behavior restoration by aligning disentangled task-relevant representations within the diffusion model. we propose a normal representation-guided conditional diffusion model for unsupervised VAD by aligning normal-specific representations. Inspired by prior knowledge of anomaly discrimination, we decompose normal behavior features into normal-specific and VAD-irrelevant representations into independent channels based on contrastive learning. We introduce a group-supervised learning strategy in learning patch-wise generation guided by normal-specific representations. A gradient-based representation alignment loss enforces the alignment between normal semantics and the target patches. This process enables the diffusion model to understand normal patterns for anomaly detection. Extensive experimental results conducted on VAD benchmarks demonstrate the effectiveness of our methods.

Decentralized anonymous payment (DAP) solves the privacy leakage problem in decentralized payment. However, some criminals may use DAP to carry out illegal activities, since DAP supports unconditional privacy protection and illegal transactions are difficult to be identified and tracked. Some studies have introduced regulatory mechanisms in DAP to track illegal transactions, but there are still problems such as privacy disclosure and low regulatory efficiency. In this paper, we propose an efficient two-level supervision scheme ETLS, which aggregates anonymous transactions based on Walsh commitment. The first-level regulator only needs to process the aggregation results to screen out suspicious users, and the second-level regulator discloses the public keys of suspicious users. During the supervision process, only the public keys of suspicious users will be identified, and the privacy of compliant transactions will be kept confidential. Compared to previous works, the ETLS scheme greatly improves regulatory efficiency while ensuring the privacy of transaction address and payment amount. The security properties of the ETLS scheme are defined and proved based on the security of DAP system, the security of Walsh commitment and zero-knowledge proof, and its performance is tested based on the Zcash system. The findings demonstrate that the ETLS scheme can effectively strike a compromise between privacy protection and regulatory requirements while maintaining low computational and communication overheads.

Generative artificial intelligence has made great progress in enabling clients to create a variety of realistic visual content (such as images, videos and audios), where diffusion model as an emerging generative model can obtain higher quality images than generative adversarial networks (GANs). For resource-constrained devices, high-definition images can be generated by outsourcing the AI model to the server, but the client’s local prompts and generated images may contain private information, increasing the risk of privacy disclosure. In order to alleviate these concerns, we adopt the idea of split learning and homomorphic encryption technology to ensure the privacy of data and prevent the attacker from stealing. Specifically, adopt homomorphic encryption to ensure the confidentiality of the text embedding and prevent the server from obtaining the prompt and text embedding. Secondly, during the process of denoising, a secure cross-attention mechanism is designed for ciphertext based on the embedding matrix to ensure the normal follow-up steps and prevent denoising model parameters from leaking to clients. In addition, since the decoder is deployed locally, the image generated by the denoising model is latent spatial features rather than pixel spatial features, so the generated image is not visible to the server. Finally, through the theoretical analysis and quantitative index of experiments, it is proved that the image generated under ciphertext prompt is almost the same as the image generated under plaintext prompt, which indicates the security and effectiveness of the proposed protocol.

Federated learning (FL) is widely used in neural network-based deep learning, which allows multiple users to jointly train a model without disclosing their data. However, the data quality of the users is not uniform, and some users with poor computing ability and outdated equipments called irregular ones may collect low-quality data and thus reduce the accuracy of the global model. In addition, the untrusted server may return wrong aggregation results to cheat the users. To solve these problems, we propose a verifiable privacy-preserving FL protocol with irregular users (VPFLI) based on single server. The protocol is privacy-preserving for the untrusted server and it is proved secure based on drop-tolerant homomorphic encryption. For low-quality datasets, their proportion would be decreased in the aggregation results in order to ensure the accuracy of the global model. Also, the aggregation results can be effectively verified by the users based on linear homomorphic hash. Moreover, VPFLI is proposed based on single server, which is more applicable in reality compare with the previous ones based on two non-colluding servers. The experiments show that VPFLI improves the accuracy of the model from 83.5% to 91.5% based on MNIST dataset compared to the traditional FL protocols.

Abstract Off-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

Remote heart rate (HR) estimation via facial videos has emerged as an attractive application for healthcare monitoring. Long short-term memory (LSTM) is a kind of deep recurrent neural network architecture used for modeling sequential information, which can be utilized for remote HR estimation. Outsourcing computation is an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for data modeling and analysis, but the risks of privacy leakage cannot be ignored. Prior privacy-preserving LSTM-based protocols only protect sensitive data or model parameters, or approximate nonlinear functions with somewhat utility degradation. To mitigate the aforementioned issues, we propose a verifiable outsourcing protocol of LSTM training for HR estimation (VOLHR) based on batch homomorphic encryption, which not only ensures the confidentiality of the local data and model parameters but also guarantees the verifiability of the model predictions to resist the deceptive attacks. Theoretical analysis proves that VOLHR provides a higher level of privacy protection than the prior works. Computational cost analysis demonstrates that VOLHR can reduce the computational overhead greatly compared with the original LSTM models with different structures. To exhibit the practical utility, we implement VOLHR on two real-world data sets for HR estimation. Extensive evaluations demonstrate that VOLHR achieves almost the same performance as the original LSTM model and outperforms prior related protocols.

The Vehicular Social Networks (VSNs) provide passengers, drivers and vehicles with multiple services, such as safe driving, data sharing and traffic management. However, transmitting data in VSNs can expose information such as the user’s identity and location. Malicious users who tamper with shared data can even cause serious traffic accidents. Considering the privacy protection and secure transmission of shared data in VSNs, we propose a blockchain-based and fog computing-assisted data access control scheme (BFDAC). We combine the multi-authority CP-ABE algorithm with the consortium blockchain to avoid the security and trust issues in the form of centralized key management, and outsource part of the decryption calculations to roadside units (RSUs) as fog nodes to realize lightweight calculation for users. Our BFDAC scheme also supports user tracking and revocation, while users can also revoke shared data saved in the cloud. Security analysis shows that the BFDAC scheme can effectively protect the shared data. Experiments show that our BFDAC scheme reduces 32.8% in storage cost, 9.5% in encryption cost, and 57.1% in outsourced decryption cost compared to previous ones.

As location data have been increasingly adopted in location-based advertising (LBA), revealing locations to untrusted service providers has raised severe privacy concerns. Recent studies propose obfuscation mechanisms built upon geo-indistinguishability (geo-IND) to provide formal privacy guarantee. Unfortunately, due to the high degree of spatiotemporal regularity in human mobility pattern, the privacy cost will be unacceptably high in this situation, leading to accurate inference of user real locations. In this study, we identify this privacy risk in LBA scenarios under long-term and multi-platform assumption. We demonstrate an attacker can infer 75%∼90% of top-1 locations within a range of only 200 meters. To address it, we proposePrivLocAd, a novel system which can provide longitudinal privacy guarantee. The novelty of PrivLocAd stems from a novel surrogate-based obfuscation, which generates multiple surrogate locations to improve the privacy-utility trade-off. In addition, two novel obfuscation mechanisms, the two-stage Gaussian and multi-level surrogate generation mechanism in charge of surrogate generation can achieve the longitudinal privacy guarantee in intra- and inter-platform condition respectively. Our experimental results demonstrate PrivLocAd is able to defend against the attack, which reduces the inference rate to less than 1% of user top-1 locations in the 200 meter range.

Outsourced inference services have greatly promoted the popularization of deep learning, and neural network models can help users customize a series of personalized applications, e.g., face recognition, image classification, etc. However, untrustworthy service providers also bring a variety of security issues, such as data privacy, network model privacy, etc. Meanwhile, the malicious example will result in the output of model incorrectly. For the above concerns, this paper proposes a privacy-reserving neural network inference against adversarial example attack (PPNNI) under two non-colluding cloud servers, where a series of efficient security protocols are designed to realize private prediction based on the additive secret sharing protocol. In the semi-honest model, the servers implement the neural network's private inference in the context of an unknown input and network model. Moreover, in order to prevent malicious examples from affecting the model accuracy, we produce a method of kernel density estimation and uncertainty value in the last layer of the hidden layer to determine whether the input is adversarial examples in the domain of ciphertext. The proposed PPNNI protocol is the first effort to efficiently detect adversarial attack behaviors under the ciphertexts. The theoretical analysis illustrate that the protocol can guarantee the privacy of input data, model parameters and the inference result. The results of the experiments demonstrate that the model accuracy are about 89% and 83% respectively with malicious examples on MNIST and CIFAR10 in the domain of ciphertext and they are nearly same as 91% and 85% in the domain of plaintext, which shows the effectiveness of our protocol.

Neural style transfer (NST) can quickly produce impressive artistic images, which allows ordinary people to become painter. The brushstrokes of stylized images created by the current NST methods are often unpredictable, which does not conform to the logic of the artist's drawing. At the same time, the style distribution of the generated stylized image texture differs from the real artwork. In this paper, we propose an interactive image style transfer network (IIST-Net) to overcome the above limitations. Our IIST-Net can generate stylized results for brushstrokes in arbitrary directions guided by graffiti curves. The style distribution of these stylized results is closer to the real-life artwork. Specifically, we design an Interactive Brush-texture Generation (IBG) module in IIST-Net to progressively generate controllable brush-textures. Then, two encoders are introduced to embed the interactive brush-textures into the content image in the deep space for producing the fused content feature map. The Multilayer Style Attention (MSA) module is proposed to further distill multi-scale style features and transfer them to the fused content feature map for obtaining the final stylized feature map with controllable brushstrokes. Additionally, we adopt the content loss, style loss, adversarial loss and contrastive loss to jointly supervise the proposed network. Experimental comparisons have demonstrated the effectiveness of our proposed method for creating controllable and realistic stylized images.

Few-shot semantic segmentation is a technique that is receiving increasing attention. The aim of this approach is to enable models to segment objects with a few support images (usually 1, 5, 10, etc.). At present, few-shot semantic segmentation has made great progress in the field of Natural Scene Image (NSI), but these methods cannot be applied directly to the field of Remote Sensing Image (RSI). In order to overcome this challenge, we propose a novel semantic segmentation network structure that integrates prototype information with global edge information to achieve more accurate prototype matching results. In addition, we design a comprehensive weighted loss function to monitor the training process to help overcome the challenges. Results of the performance comparison with state-of-the-art few-shot semantic segmentation methods demonstrate the superiority of the proposed method.

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

Face recognition has been extensively employed in practice, such as attendance system and public security. Linear discriminant analysis (LDA) algorithm is one of the most significant ones in the field of face recognition, but it is very difficult for many clients to employ it in their resource-constrained devices (e.g., smartphones and notebook computers). Outsourcing computation provides a promising method for clients to perform heavy tasks with limited computing power. In this paper, we design a protocol of outsourcing LDA-based face recognition to an untrusted cloud, which can help the client to complete the operations of matrix inversion (MI), matrix multiplication (MM) and eigenvalue decomposition (ED) simultaneously. The proposed outsourcing protocol can hide the private data of the client from the cloud. More importantly, the client can verify whether the outsourcing results are correct or not with probability one and so it is impossible for the server to deceive the client. In addition, the proposed protocol greatly decreases the computational complexity of the client thus enabling the client to complete LDA algorithm efficiently. Finally, we implement the protocol and give a comprehensive evaluation. The experimental results demonstrate that the client obtain great computing savings and the face recognition accuracy in the proposed protocol is almost identical to the original LDA algorithm.

Rotation invariant local binary pattern (RI-LBP) features have been applied in diverse scenarios with the advantages of gray-scale and rotation invariance. Secure fog computing has become an emerging paradigm for enterprises or individuals with a huge volume of private data, but limited computing power for feature extraction. Prior secure outsourcing protocols based on LBP and RI-LBP simply focus on local data privacy, which can only resist ciphertext-only attack, and also make extracted features exposed to the cloud. This work focuses on how to effectively ensure data confidentiality and feature integrity. We propose a verifiable privacy-enhanced protocol for RI-LBP feature extraction (VRLBP) based on the fog computing paradigm, which mitigates the aforementioned challenges by involving the proposed symmetric cryptographic scheme where local data and extracted features are proven secure against chosen plaintext attack. Meanwhile, the stage of verification can check the correctness of outsourced features with an overwhelming probability and constant computational complexity. The security analysis and computational costs demonstrate that VRLBP can reduce the computation overhead to around 30% of original feature extraction in a privacy-preserving manner. To exhibit the practical utility, VRLBP is implemented for deepfake detection on five public datasets. Extensive evaluations indicate that VRLBP achieves almost the same accuracy as the original RI-LBP algorithm and outperforms the state-of-the-art protocols.

Symmetric nonnegative matrix factorization (SNMF) is effective to cluster nonlinearly separable data, which uses the constructed graph to capture the structure of inherent clusters. Nevertheless, many SNMF-based clustering approaches implicitly enforce either the sparseness constraint or the smoothness constraint with the limited supervised information in the form of cannot-link or must-link in a semi-supervised manner, which may not be quite satisfactory in many applications where sparseness and smoothness are demanded explicitly and simultaneously. In this paper, we propose a new semi-supervised SNMF-based approach termed Semi-supervised Structured SNMF-based clustering (S3NMF). The method flexibly enforces the block-diagonal structure to the similarity matrix, where the sparseness and smoothness are simultaneously considered, so that we can obtain the desirable assignment matrix by simultaneously learning similarity and assignment matrices in a constrained optimization problem. We formulate S3NMF with a semi-supervised manner and utilize the indirect constraints of sparseness and smoothness by cannot-link and must-link. To effectively solve S3NMF, we present an alternating iterative algorithm with theoretically proved convergence to seek for the solution of the optimization problem. Experiments on five benchmark data sets show better performance and satisfactory stability of the proposed method.

As the underlying technology of cryptocurrencies, blockchain has gained a lot of attention in recent years. However, the storage problem needs to be solved with the increasing number of blocks in the blockchain network. Cloud storage optimization is an effective way to solve the storage issue, which selects and stores parts of blocks to the cloud. Precisely, block selection can be described as a multiobjective optimization problem (MOP) and solved by evolutionary algorithms (EAs). To obtain well results of block selection, an improved NSGA-III algorithm based on deep Q-networks (DQN), termed NSGA-DQN, is proposed in this paper, which aims to maintain well convergence and diversity of the population. This way, a set of suitable solutions is obtained to determine the number of blocks stored to the cloud, and the storage problem can be solved effectively. To be specific, DQN creates a decision-making agent to maximize the expected reward by learning a policy that evaluates$Q$values of each action in each state. In the proposed selection mechanism, the reward values are set according to the convergence and diversity of the population, and the actions correspond to the individuals. This way, our method can determine a set of individuals that maximizes the convergence and diversity of the population. In addition, an adaptive maximum reward enhancement module (AMREM) is developed to further enhance the maximum expected reward by updating the new better reward and modifying the replay memory. We conduct the experimental study on block selection, and the results demonstrate that the proposed algorithm is superior to five state-of-the-art algorithms.

Federated learning (FL) is a distributed machine learning framework, which allows multiple users to collaboratively train and obtain a global model with high accuracy. Currently, FL is paid more attention by researchers and a growing number of protocols are proposed. This article first analyzes the security vulnerabilities of the VerifyNet and VeriFL protocols, and proposes a new aggregation protocol for FL. We use additive homomorphic encryption and double masking to simultaneously protect the user’s local model and the aggregated global model while most of the existing protocols only consider the privacy of the local model. Also, linear homomorphic hash and digital signature are used to achieve traceable verification, which means the users can not only verify the aggregation results, but also be able to identify the wrong epoch if the results are wrong. In summary, our protocol can realize the privacy of the local model and global model and achieve verification traceability even if the cloud server colludes with malicious users. The experimental results show that the proposed protocol improves the security of FL without decreasing the efficiency of the users and classification accuracy of the training model.

In the traditional blockchain system, data is public and cannot be redacted. With the development of blockchain technology, the problem that the data cannot be altered will be more serious once it is written on the chain. Recently, some redactable blockchain schemes have been proposed. However, most of the schemes are based on the public blockchain, and the users’ identities and transaction data may be disclosed. To solve the problem of privacy disclosure, we propose a privacy-preserving transaction-level redactable blockchain. In the proposed scheme, symmetric encryption and ring signature are used to protect transaction data and the users’ identities, respectively. In order to prove the legality of data redaction, the transaction sender can reveal the invalid users’ identities and transaction data in an anonymous environment. To construct a transaction-level redactable blockchain, the users only need to replace a single transaction to complete the data redaction instead of replacing the entire block. The experimental results show that the proposed scheme saves 20% of the redaction time compared to the previous privacy-preserving blockchains, so the redaction efficiency is higher.

QR and LU factorizations are two basic mathematical methods for decomposition and dimensionality reduction of large-scale matrices. However, they are too complicated to be executed for a limited client because of big data. Outsourcing computation allows a client to delegate the tasks to a cloud server with powerful resources and therefore greatly reduces the client’s computation cost. However, the previous methods of QR and LU outsourcing factorizations need multiple interactions between the client and cloud server or have low accuracy and efficiency in large-scale matrix applications. In this paper, we propose a noninteractive and efficient outsourcing algorithm of large-scale QR and LU factorizations. The proposed scheme is based on the specific perturbation method including a series of consecutive and sparse matrices, which can be used to protect the original matrix and obtain the results of factorizations. The generation and inversion of sparse matrix has small workloads on the client’s side, and the communication cost is also small since the client does not need to interact with the cloud server in the outsourcing algorithms. Moreover, the client can verify the outsourcing result with a probability of approximated to 1. The experimental results manifest that as for the client, the proposed algorithms reduce the computational overhead of direct computation successfully, and it is most efficient compare with the previous ones.

Content-adaptive steganography embeds information into the cover image adaptively under the guidance of embedding probabilities (also known as selection channel) of the image elements, which can also be obtained by the steganalyzer. Therefore, some adaptive steganalysis schemes with selection channel have been proposed to detect content-adaptive steganography. Existing selection-channel-aware CNN-based steganalyzers usually incorporate the embedding probability into the first convolutional layer. They may fail to make full use of embedding probability information because this information incorporated into the first layer may disappear when it propagates to deep convolutional layers. To address this issue, we propose embedding probability guided module to adaptively enhance the features from different levels of network. Moreover, these embedding probability guided features from different levels are progressively integrated to jointly make final decisions. Experimental results prove that our approach can outperform existing selection-channel-aware approaches in both the spatial domain and JPEG domain.

Outsourcing computation allows an outsourcer with limited resource to delegate the computation load to a powerful server without the exposure of true inputs and outputs. It is well known that modular exponentiation is one of the most expensive operations in public key cryptosystems. Currently, most of outsourcing algorithms for modular exponentiation are based on two untrusted servers or have small checkability with single server. In this paper, we first propose an efficient outsourcing algorithm of modular exponentiation based on two untrusted servers, where the outsourcer can detect the error based on Euler theorem with a probability of 1 if one of the servers misbehaves. We then present an outsourcing algorithm of modular exponentiation with single server, and the outsourcer can also check the failure with a probability of 1. Therefore, the proposed algorithm with single server improves efficiency and checkability simultaneously compare with the previous ones. Finally, we provide the experimental evaluations to demonstrate that the proposed two algorithms are the most efficient ones in all of the outsourcing algorithms for an outsourcer.

With the rise of cryptocurrencies, blockchain, which is the underlying technology of them, has gained more attention and been used in the Internet of Things (IoT) and other fields. However, there are bottlenecks that hinder its application, such as the storage capacity. Due to the large number of IoT devices which always act as data generators in many systems, the transactions will be generated at a high rate. The storage problem will be more serious in IoT. In this article, to expand the capacity of blockchain, for each peer, we select old blocks which are created previously and less likely to be queried and store them in the cloud. Based on this idea, we develop objective functions related to query probability, storage cost, and local space occupancy, which naturally narrows down the problem to block selection. The results can be obtained by solving a multiobjective optimization problem. We design a nondominated sorting genetic algorithm with clustering (NSGA-C), which changes the method of selecting individuals from the critical dominance layer by adding clustering to ensure diversity. A suitable solution can be selected from the Pareto set to fulfil the requirements of different users. We then compare the algorithm with the improved NSGA-II and NSGA-III, which both add integer constraints to the decision variables. The results show that our method is better than NSGA-II and NSGA-III in terms of local space occupancy in the blockchain application. In addition, it can also effectively avoid the risk of block overflow.

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.

With the widespread use of biometrics recognition systems, it is of paramount importance to protect the privacy of biometrics. In this paper, we propose to protect the fingerprint privacy by the artificial fingerprint, which is generated based on three pieces of information, i) the original minutiae positions; ii) the artificial fingerprint orientation; and iii) the artificial minutiae polarities. To make it real-look alike and diverse, we propose to generate the artificial fingerprint orientation by a model taking both the global and local fingerprint orientation into account. Its parameters can be easily guided by an user specific key with simple constraints. The artificial minutiae polarities are generated from the same key, where a block based and a function based approach are proposed for the minutiae polarities generation. These information are properly integrated to form a real-look alike artificial fingerprint. It is difficult for the attacker to distinguish such a fingerprint from the real fingerprints. If it is stolen, the complete fingerprint minutiae feature will not be compromised, and we can generate a different artificial fingerprint using another key. Experimental results show that the artificial fingerprint can be recognized accurately.

An important and long-standing question in computational learning theory is how to learn $\textrm{AC}^0$ circuits with respect to any distribution (i.e. PAC learning). All previous results either require that the underlying distribution is uniform Linial et al. (1993) (or simple variants of the uniform distribution) or restrict the depths of circuits being learned to 1 Valiant (1984) and 2 Klivans and Servedio (2004). As for the circuits of depth 3 or more, it is currently unknown how to PAC learn them. \newline In this paper we present an algorithm to PAC learn depth-3 $\textrm{AC}^0$ circuits of bounded top fanin over $(x_1,\cdots,x_n,\overline{x}_1,\cdots,\overline{x}_n)$. Our result is that every depth-3 $\textrm{AC}^0$ circuit of top fanin $K$ can be computed by a polynomial threshold function (PTF) of degree $\widetilde{O}(K\cdot n^{\frac{1}{2}})$, which means that it can be PAC learned in time $2^{\widetilde{O}(K\cdot n^{\frac{1}{2}})}$. In particular, when $K=O(n^{\epsilon_0})$ for any $\epsilon_0<\frac{1}{2}$, the time for learning is sub-exponential. We note that instead of employing some known tools we use some specific approximation in expressing such circuits in PTFs which can thus save a factor of $\textrm{polylog}(n)$ in degrees of the PTFs.

It is well known that the computation of bilinear pairing is the most expensive operation in pairing-based cryptography. In this paper, we propose a noninteractive verifiable outsourcing algorithm of bilinear pairing based on two servers in the one-malicious model. The outsourcer need not execute any expensive operation, such as scalar multiplication and modular exponentiation. Moreover, the outsourcer could detect any failure with a probability close to 1 if one of the servers misbehaves. Therefore, the proposed algorithm improves checkability and decreases communication cost compared with the previous ones. Finally, we utilize the proposed algorithm as a subroutine to achieve an anonymous identity-based encryption (AIBE) scheme with outsourced decryption and an identity-based signature (IBS) scheme with outsourced verification.

The problem of securely outsourcing computation has received widespread attention due to the development of cloud computing and mobile devices. In this paper, we first propose a secure verifiable outsourcing algorithm of single modular exponentiation based on the one-malicious model of two untrusted servers. The outsourcer could detect any failure with probability 1 if one of the servers misbehaves. We also present the other verifiable outsourcing algorithm for multiple modular exponentiations based on the same model. Compared with the state-of-the-art algorithms, the proposed algorithms improve both checkability and efficiency for the outsourcer. Finally, we utilize the proposed algorithms as two subroutines to achieve outsource-secure polynomial evaluation and ciphertext-policy attributed-based encryption (CP-ABE) scheme with verifiable outsourced encryption and decryption.

In an identity-based encryption (IBE) scheme, how to revoke users from the system is a difficult problem when their private keys are compromised. The private key generator (PKG) updates the private keys for all unrevoked users and has high computation load when a large number of users are included. We propose an IBE scheme with verifiable outsourced revocation based on the one-malicious model of two servers. In the proposed scheme, PKG delegates the key update operations to the two servers for all unrevoked users. The PKG can detect the failure with probability 1 if one of the servers misbehaves. Our scheme is proven fully secure and verifiable against chosen-plaintext attack (CPA) without random oracles. The servers cannot execute the key update operations for any revoked user even if they collude. The experiment shows the time cost for PKG in the outsourcing algorithm is much smaller than that for directly updating the private keys for all unrevoked users.

This paper proposes a novel scheme of compressing encrypted images with auxiliary information. The content owner encrypts the original uncompressed images and also generates some auxiliary information, which will be used for data compression and image reconstruction. Then, the channel provider who cannot access the original content may compress the encrypted data by a quantization method with optimal parameters that are derived from a part of auxiliary information and a compression ratio-distortion criteria, and transmit the compressed data, which include an encrypted sub-image, the quantized data, the quantization parameters and another part of auxiliary information. At receiver side, the principal image content can be reconstructed using the compressed encrypted data and the secret key. Experimental result shows the ratio-distortion performance of the proposed scheme is better than that of previous techniques.

This paper proposes a novel scheme of scalable coding for encrypted images. In the encryption phase, the original pixel values are masked by a modulo-256 addition with pseudorandom numbers that are derived from a secret key. After decomposing the encrypted data into a downsampled subimage and several data sets with a multiple-resolution construction, an encoder quantizes the subimage and the Hadamard coefficients of each data set to reduce the data amount. Then, the data of quantized subimage and coefficients are regarded as a set of bitstreams. At the receiver side, while a subimage is decrypted to provide the rough information of the original content, the quantized coefficients can be used to reconstruct the detailed content with an iteratively updating procedure. Because of the hierarchical coding mechanism, the principal original content with higher resolution can be reconstructed when more bitstreams are received.

This paper proposes a novel watermarking scheme with flexible self-recovery quality. The embedded watermark data for content recovery are calculated from the original discrete cosine transform (DCT) coefficients of host image and do not contain any additional redundancy. When a part of a watermarked image is tampered, the watermark data in the area without any modification still can be extracted. If the amount of extracted data is large, we can reconstruct the original coefficients in the tampered area according to the constraints given by the extracted data. Otherwise, we may employ a compressive sensing technique to retrieve the coefficients by exploiting the sparseness in the DCT domain. This way, all the extracted watermark data contribute to the content recovery. The smaller the tampered area, the more available watermark data will result in a better quality of recovered content. It is also shown that the proposed scheme outperforms previous techniques in general.