This paper proposes ROFCL, a robust one-bit federated compressive learning framework that mitigates compression distortion and channel noise over parallel binary symmetric channels. It features a client-side quantization-aware learnable coding to minimize compression distortion during training, and a server-side trust-weighted likelihood aggregation that performs robust information aggregation. In this method, quantization-aware learning within a single client, cross-client diversity within a single round, and accumulated history reputation across multiple rounds jointly achieve robustness in extreme one-bit compression.

Federated learning (FL) enables multi-institutional MRI reconstruction without centralizing patient data, serving as a fundamental privacy-preserving technology for medical multimedia analysis and retrieval. However, practical deployment of FL-based reconstruction in clinical retrieval pipelines is hindered by three key obstacles: prohibitive edge computation costs, statistical heterogeneity that distorts diagnostic features, and the server blindness inherent in Secure Aggregation (SecAgg) which complicates quality auditing. In this paper, we propose FedLUR, a secure, lightweight, and intelligent federated framework designed to restore high-fidelity images for reliable downstream retrieval. FedLUR freezes a compact, physics-consistent deep unfolding backbone and communicates only lightweight visual prompts, reducing communication overhead compared to full-model baselines. To ensure the semantic consistency required for accurate image retrieval, we introduce a null-space constrained optimization strategy that stabilizes prompt tuning against client drift, thereby preserving global diagnostic structures. Furthermore, to reconcile SecAgg with utility-aware coordination, FedLUR utilizes a deterministic Large Language Model (LLM) agent to calibrate aggregation weights based on privacy-friendly metadata, ensuring robust convergence without exposing raw updates. Experiments demonstrate that FedLUR achieves state-of-the-art reconstruction quality. A proxy retrieval test shows highly consistent features between reconstructed and ground-truth images, supporting downstream medical multimedia retrieval.

Federated collaborative filtering enables privacy-preserving recommendation systems but faces challenges in capturing high-order interactions, reducing communication overhead, and minimizing accuracy degradation. To address these issues, we propose Federated Compressive Collaborative Filtering (FCCF), a novel framework that leverages tensor compressive learning for cross-client predictions. FCCF employs a tensor-based model with GNN-based extraction to efficiently represent multi-type item and dual-role user nodes and introduces a sketch-to-embedding projection for feature analysis. It performs inference directly on compressed data, eliminating the need for precise signal reconstruction, and employs client-specific sampling matrices along with regularization to enhance privacy and preserve local representations. Experiments demonstrate FCCF’s effectiveness in improving prediction accuracy, robustness to privacy noise, and communication efficiency.

Federated Learning enables collaborative model training across multiple devices without sharing raw data. However, when private data originates from multiple domains with different feature distributions, the model's generalization ability on unseen domains can deteriorate. Moreover, existing methods often overlook privacy protection and communication overhead during model performance enhancement. To address these challenges, we propose the CSPFed Framework. First, we introduce Federated Prototype Learning to improve the generalization of the Global Model on private domains. Next, we employ improved Compressed Sensing for lossy compression of the model. Finally, we propose a block-based CKKS Homomorphic Encryption Algorithm to encrypt data transmitted between clients and the server. This framework significantly reduces communication overhead while enhancing model accuracy, all while ensuring data privacy. CSPFed outperforms the best competitors by 5.59 % on two public datasets. Empirical results demonstrate the effectiveness of the proposed solution and the efficiency of its key components.

Federated learning (FL) enables participants to collaboratively train models without compromising the privacy of their local data. However, it remains vulnerable to threats such as Byzantine attacks and privacy leaks. These challenges are inherently interdependent: privacy-preserving mechanisms limit access to model updates to protect private information, whereas Byzantine-robust techniques need such access for thorough analysis. Existing research has developed FL for protecting privacy and resisting Byzantine attacks, but most of these methods are effective only under the honest majority premise or based on the non-collusion assumption among servers, and a few of them require the assistance of trusted third parties (TTPs). To address these challenges, we propose a Byzantine-robust and privacypreserving FL framework (BRPPFL), which identifies Byzantine attackers in the compressed domain of compressed sensing (CS) and has the advantage of low communication overhead. To mitigate privacy leaks, BRPPFL combines secure two-party computation (2PC) protocols with homomorphic encryption (HE) primitives to create a privacy-preserving secure aggregation protocol. Additionally, it minimizes computational overhead by offloading complex operations to the offline phase. Experimental results demonstrate that BRPPFL can accurately and efficiently identify Byzantine attackers while protecting privacy. Even at an extreme compression rate (CR) of 0.1, our framework maintains acceptable defense performance.

Compressed sensing (CS) is a powerful signal pro-cessing technique that can simultaneously realize signal sampling, compression, and lightweight encryption. However, traditional CS reconstruction algorithms often face challenges such as high signal sparsity requirements, poor reconstruction accuracy, and low efficiency. Meta-learning, which optimizes parameter selection and update strategies during iterations, can enhance the efficiency and convergence speed of CS reconstruction. In this article, we present a Meta-learning-based Compressed Sensing Reconstruction method in the Cloud (MetaCSRC), an end-to-end imaging paradigm designed to address these challenges. We develop an adaptive sampling network for CS sampling on the local client and a deep meta-learning convolutional network for CS loss optimization on the cloud server. By leveraging the cloud's sufficient computing and storage resources, the computationally intensive reconstruction process is offloaded from the clients. Additionally, we incorporate local differential privacy noise into the measurements before uploading them to the cloud server to enhance the security of measurement transmission. Experimental results demonstrate that MetaCSRC excels in both reconstruction speed and accuracy, while also providing enhanced privacy protection.

Federated Learning (FL) has received widespread attention as a collaborative learning paradigm. Clients can collaboratively train a global model with server by uploading parameters instead of sharing the raw data, which guarantees the basic data privacy. However, recent research has highlighted that sensitive information can be inferred from shared updates or gradients, resulting in serious privacy leakage. Moreover, transmitting the updates or gradients can result in communication bottlenecks. In order to solve the privacy and communication problems, we propose a federated multilinear compressed learning framework (FedMCL), which considers the tensor structure of the data and performs multidimensional compression on the client’s raw data through multilinear compressed learning. Compared with vector-based compressed learning, it has better learning performance on multi-dimensional data. We generate proxy images from the measurement domain for training, which effectively defends against gradient inversion attacks. In addition, we introduce low-rank approximation method to compress model updates and reduce the communication overhead by transmitting small matrices instead of the original model updates. Experimental results show that our scheme can resist gradient inversion attacks under different compression rates while obtaining good classification performance, which has advantages in terms of privacy, performance and communication.

The rapid advances in deep learning have enabled the extraction of soft-biometric attributes from faces, raising privacy concerns regarding images collected for face recognition. To address this issue, we propose a privacy-preserving scheme for multiple soft-biometric attributes using Generative Adversarial Networks (GANs), aiming to effectively protect individual privacy while maintaining face recognition system performance. Our scheme incorporates two main modules: 1) The attribute encryption system, which identifies attributes to be hidden through chaotic cryptography and supports the recovery of original attributes. 2) Encoder features are adaptively transformed using Selective Transmission Units (STUs) within a generator featuring an encoder-decoder structure to generate perturbed images. The generated perturbed images are visually similar to the original images but differ in soft-biometric attributes. Experimental results show that our method maintains high face recognition accuracy while preserving privacy, and the quality of the generated images surpasses existing techniques.

Federated learning (FL) facilitates collaborative training of a global model without sharing the participants’ raw data. Nevertheless, existing FL approaches still face three major issues: 1) How to propose a more efficient and secure privacy-preserving method; 2) How to verify the identity of participants to ensure they are not impersonators; 3) How to reduce the significant communication cost. To address the aforementioned concerns, several schemes have been proposed. However, these schemes suffer from flaws in security, efficiency, and functionality. Furthermore, few researches have considered the possibility of adversaries impersonating legitimate participants to undermine the integrity and availability of the model or launch a free-riding attack. In this paper, we first combine the advantages of secret sharing, Diffie-Hellman key agreement, and functional encryption to develop an authenticable secure multi-party computing algorithm (SDF-ASMC). This algorithm can guarantee the security of transmitted data and provide authentication functionality in the absence of a trusted third party. Moreover, an efficient, secure, and authenticable FL algorithm (ESAFL), which leverages compressed sensing and all-or-nothing transform, is introduced to reduce the transmission and encryption of local gradients. Then, only the final element of the transformed measurements is encrypted by our proposed SDF-ASMC to protect all the measurements. This method effectively improves the efficiency of our algorithm. In addition, ESAFL also tolerates participants’ dropout. Security analysis demonstrates that our proposed algorithms can securely aggregate local gradients. Finally, the extensive experiments demonstrate the practical performance of our proposed algorithms.

Federated learning (FL) enables decentralized industrial-Internet-of-Things devices (also called clients) to share model parameters to build a joint model. Fair rewards, security of shared data, and transmission cost are the important factors that influence clients to participate in FL. Few existing works can solve these problems at the same time. Therefore, we propose a hierarchically fair and differentially private federated learning (HFDPFL), which regards the model itself as a reward to promote fairness. Reputation is used to measure the client's contribution to FL, and clients with high reputation will be rewarded with high accuracy models. In order to ensure the security of the shared data and reduce communication overhead, we implement differentially private gradient compression based on compressed sensing, which achieves differential privacy protection of gradients and improves communication efficiency. Extensive experiments are conducted to demonstrate the superiority of HFDPFL in terms of fairness, privacy preserving, and communication efficiency.

Compressive sensing (CS), a breakthrough technology in image processing, provides a privacy-preserving layer and image reconstruction while performing sensing and recovery processes, respectively. Unfortunately, it still faces high-complexity, low-security, and low-quality reconstruction challenges during image processing. Therefore, this article presents a secure low-complexity CS scheme with preconditioning prior regularization reconstruction. More specifically, the original image is compressed by a low-complexity LFSR-based sparse circulant matrix to obtain measurements. It is worth noting that measurements achieve preliminary distribution equalization through the Tanh sequence to acquire processed measurements. Furthermore, the privacy-preserving edge processing for processed measurements can achieve high security. Finally, preconditioning prior regularization CS reconstruction is designed to improve reconstruction performance. Simulation results and analyses demonstrate that the proposed scheme can achieve low-complexity sampling, high security, and superior reconstruction performance.

Data collection and sharing have a tremendous impact on technology, business and society. Correspondingly, it brings in significant privacy and communication concerns. To this end, we present a hierarchical privacy-preserving and communication-efficient compression scheme via compressed sensing (CS) to address these two issues. In the encoding stage, the obfuscated sensitive regions and non-sensitive regions are compressed and encrypted simultaneously. Consequently, the semi-authorized users and authorized users are considered in the decoding stage. Additionally, the left annihilator matrices provide various kinds of recovery qualities for real-world requirements, which further achieves communication-efficient compression.

Compressed sensing (CS) has attracted wide attention in signal process field, which states that a sparse signal or a compressive signal can be reconstructed by using a small number of CS samples efficiently. In recent years, CS-based image coding methods have been investigated extensively. However, the ratio-distortion (R-D) performance of this method is unsatisfactory in comparison with traditional compression algorithms, such as JPEG and JPEG2000.

Watermarking schemes in plaintext domain usually suffer from high exposure risk of the cover signal. Data hiding in encrypted image (DHEI) can guarantee the security of the cover signal by embedding secret information into encrypted signal. Based on the above, we propose a robust watermarking scheme in encrypted domain based on integer lifting wavelet transform (LWT) and compressed sensing (CS). Firstly, the signal owner uses Kronecker CS progressive reconstruction and global random permutation (GRP) to realize the reserving room and encryption. Subsequently, the watermark is preprocessed by integer LWT, and compressed by CS. Then, the watermark is embedded by multiple embedding method (MEM) and most significant bits (MSBs) replacement in the cover signal. Experiments show that the proposed scheme can extract the watermark stably under a variety of attacks while the cover signal achieves satisfactory reconstruction.

Federated learning (FL) offers a solution for mitigating the issue of data silo. However, FL faces threats to both robustness and privacy, which hinder the widespread application of FL. Most existing approaches focus on one of these threats or require significant resources to tackle both simultaneously. To meet the requirements of robustness and privacy, we propose a robust and privacy-preserving FL (RPFL) based on random selection and lightweight sharing. Our random selection method effectively invalidates malicious models to protect the integrity of the global model. On the other hand, we employ the technique of multi-party computation (MPC) to enhance privacy. To mitigate additional communication overhead and computation overhead introduced by MPC, we propose lightweight sharing. Besides, we adopt compressed sensing and parameter-clipping to further improve the communication efficiency and robustness of RPFL. We prove the performance of RPFL in terms of robustness, privacy, as well as efficiency. The extensive experimental results demonstrate that RPFL effectively improves the robustness and privacy of FL with only a negligible performance penalty.

Trust-aware recommendation plays an essential role in alleviating information overload by exploiting social relationships among users to build recommendation systems. However, recommendation systems in mobile social networks suffer from the sparse trust problem, which severely affects the reliability of trust propagation and the accuracy of the recommendation. The flourishing graph neural networks have revitalized Trust-aware Recommendation Systems. Therefore, we propose a sparse trust data mining method based on graph attention networks (TrustGAT) to mine potential trust information between entities in large-scale mobile social networks. First, the potential relationships between the trustors and the trustees are simulated in sparse data, and thereof an adaptive trust network is built. On this basis, features of trust-related information and items are learned in multi-head attention modules to enable the scheme stability. In addition, the implicit influence of trust is introduced to augment network node representation. Empirical results on three public benchmarks show that TrustGAT can make recommendation for users rapidly and accurately as well as alleviate the sparse trust problem effectively.

Although the extensive application of the Internet of Things brings great convenience, it raises the concern of privacy leakage in the processes of data acquisition, analyzing, and sharing as well. In this article, multilevel privacy protection via compressed sensing (CS) is proposed, which has the advantages of compressed sampling, protection of data privacy, and controllability of data access. At the data acquisition end, the CS technique suitable for a resource-constrained environment is employed to sample and encrypt signals with the assistance of discriminant component analysis. Then, the encrypted data will be transmitted to the cloud in time. On the cloud service, signals protected by CS rarely disclose private information to malicious attackers, and they will be accessed by two-class authorized entities. One is the semiauthorized user with low privilege who can only get the features from encrypted data for the subsequent inference; the other is the full-authorized user who is capable of reconstructing the original data. We demonstrate the scheme through two case studies of a face recognition system and a human activity recognition system and analyze its performance.

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l1 norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.

As a new signal processing technology, compressed sensing (CS) has been showed to be a promising solution for compressing cipher images. However, the previous CS-based schemes are unsatisfactory in terms of ratio-distortion (R-D) performance. In order to solve this problem, an image encryption-then-compression (ETC) scheme by using semi-tensor product CS (STP-CS) and pre-mapping is proposed in this paper. In the proposed scheme, the original image is encrypted by using the scrambling operation. After image encryption, the cipher image is compressed through three steps. Firstly, the original image is compressed by using STP-CS. Secondly, the CS samples are processed by using pre-mapping operation. Thirdly, the resultant CS samples are quantized and encoded into bits. For image signal recovery, an iterative bivariate shrinkage (IBS) algorithm is proposed. Compared with the existing CS-based image ETC schemes, the proposed scheme has better R-D performance.

Facing the explosive growth of image data, how to realize low-cost compression coding and how to protect the confidentiality of image data have become two important research topics. In this work, a novel image coding scheme is proposed, which combines compression and encryption under the framework of compressed sensing (CS). Firstly, the signal is sampled using CS, then normalized and quantified. Next, according to the statistical characteristic of the measurements, the partially quantized measurements are selected for single-valued diffusion, and at the same time the auxiliary information, including the energy information and the quantization parameters, are automatically embedded. In contrast to other existing schemes, our scheme simultaneously achieves two security objectives: one is avoiding energy leakage in CS-based cryptosystem; the other is resisting Chosen Plaintext Attack (CPA). Furthermore, without transmitting the auxiliary information, the receiver can perform decryption correctly. Experimental results and analysis also demonstrate the effectiveness and security of the proposed scheme.

Compressed sensing (CS) can be considered as a symmetric cryptosystem. However, it faces two challenges. The first challenge is privacy-preserving, including the small key space and energy leakage. The second challenge is that the measurements with different compression rates have to be transmitted to recover images for different requirements, i.e., transmission explosion. To this end, we investigate a two-layer controllable recovery strategy for privacy-preserving double-compressed images. Firstly, we employ perturbation CS to compress and encrypt the double permuted images simultaneously, which can increase key space. Secondly, a normalized quantization is designed for the fused image, and it aims to save transmission consumption and conceal energy perfectly. Finally, a two-layer controllable recovery strategy is designed for real-world requirements, which avoids the transmission of measurements with different compression rates. Our experimental results and analyses demonstrate the feasibility and effectiveness of the proposed scheme.

Data hiding in encrypted images (DHEI) embeds data in the cipher state, which not only protects the confidentiality of the cover image content, but also hides the existence of the secret information, and has a broad application prospect. However, many DHEI schemes pursue high capacity at the expense of robustness. In this letter, we propose a robust watermarking scheme for encrypted images based on scrambling and Kronecker compressed sensing (CS). The scheme makes full use of the encryption and compression characteristics of CS to realize the image encryption and preprocessing at the same time, and utilizes the democratic characteristic of CS, Most Significant Bits (MSBs) replacement, image global scrambling and adjusted uniform quantization to improve the robustness. In addition, the two-dimensional Kronecker CS reduces the computational complexity of the encoding end and increases the encoding efficiency. Experimental results show that our scheme achieves good robustness against various noise attacks and clipping attacks, while maintaining high-capacity embedding and stable recovery.

In recent years, cipher-image coding by using compressed sensing (CS) has became a hot topic. However, the ratio-distortion (R-D) performance of the previous methods are barely satisfactory. In order to address this concern, a 2D CS (2DCS) scheme by using nonlocal low-rank prior (NLP) reconstruction is proposed in this letter. Firstly, the scrambling encryption is applied to mask the plaintext image. Secondly, the cipher image is compressed by 2DCS. Lastly, an iterative singular value thresholding (ISVT) algorithm is developed, which can reconstruct the image effectively by exploring the NLP information of the image. Simulation results show that the proposed method outperforms the previous CS-based methods in terms of R-D performance.

In recent years, using compressed sensing (CS) as a cryptosystem has drawn more and more attention since this cryptosystem can perform compression and encryption simultaneously. However, this cryptosystem is vulnerable to known-plaintext attack (KPA) under multi-time-sampling (MTS) scenario due to the linearity of its encoding process. In this paper, a privacy-preserving CS scheme for image compression-encryption applications is proposed, which embeds a non-linear operation called noise injected negative-positive transformation (NINPT) in the CS encoding process with the purpose of withstanding KPA. The encoding procedures of the proposed scheme include three steps. First, the original image is encrypted by using NINPT operation. Second, the intermediate ciphertext is re-encrypted and compressed by using CS simultaneously. Third, the final compressed ciphertext is quantized into bits via scalar quantization (SQ). Since the introduction of NINPT operation in the CS encoding process breaks the linearity of the CS sampling process, the proposed scheme can withstand KPA under MTS scenario. For image signal reconstruction, a projected Landweber with embedding decryption (PL-ED) algorithm is proposed. Simulation results demonstrate that the proposed scheme can withstand KPA under MTS scenario at the cost of slightly sacrificing the compression performance.

Although P-tensor product compressed sensing (PTP-CS) breaks the strict dimension matching restriction between measurement matrix and signal in multiplication, it still faces the huge resource consumption problems of the frequent update and transmission of measurement matrix and signal reconstruction. Accordingly, we design a public measurement matrix and utilize the cloud to solve the PTP-CS reconstruction (PTP-CSR) task under privacy protection.Specifically, we propose a low complexity and secure PTP-CSR outsourcing model to protect the signal privacy, and further introduce user authentication and data verification services. In our model, the client samples the signal based on PTP-CS and uploads the encrypted measurement to the cloud. The cloud further encrypts the data asymmetrically for storage security and user management. After receiving the request, the cloud authenticates the user's identity. Once successful, the cloud processes the encrypted PTP-CSR task and returns result to users. More importantly, we provide result verification approach for users. Our experimental results demonstrate the privacy protection of the signal and the effectiveness of the proposed model.

Internet of Multimedia Things (IoMT) is facing how to achieve low-cost compression and acquisition of multimedia big data at the resource-constrained side while preserving the confidentiality of the data. In this article, we propose a low-cost and confidentiality-preserving multi-image compressed acquisition model and provide separate image reconstruction. We group a series of image sets and randomly sample them with compressive sensing in each group. It is noteworthy that we harness the sigmoid sequence to fuse the measurement of each group to alleviate the uneven distribution of the measurement. The experiment verifies that the proposed method avoids the energy information leakage of the original signal from the measurement. Subsequently, we assemble the sampling result of each group into a big master image with a suitable size and further encrypt it. The encrypted master image is transmitted to the cloud for storage and decryption service. The cloud performs a separate reconstruction of images. It provides different image reconstruction services for a fused measurement according to realistic demands, which reduces unnecessary resource consumption. Simulation results show the effectiveness and security of our acquisition scheme, which indicates the proposal can have a potential application to IoMT.

In the age of the industrial big data, there are several significant problems such as high-overhead data acquisition, data privacy leakage, and data tampering. Fog computing capability is rapidly expanding to address not only network congestion issues but data security issues. This article presents a chaotic compressive sensing (CS) scheme for securely processing industrial big image data in the fog computing paradigm, called privacy-assured FogCS. Specially, the sine logistic modulation map is used to drive the privacy-assured, authenticated, and block CS for secure image data collection in the sensor nodes. After sampling, the measurements are normalized in the fog nodes. The normalized measurements can achieve the perfect secrecy and their energy values are further masked through the proposed permutation-diffusion architecture. Finally, these relevant data are transmitted to the clouds (data centers) for storage, reconstruction, and authentication if required. In addition, a hardware implementation reference on a field programmable gate array is designed. Simulation analyses show the feasibility and efficiency of the privacy-assured FogCS scheme.

In many practical scenarios, image encryption should be implemented before image compression. This leads to the requirement of compressing encrypted images. Compressed sensing (CS), a breakthrough in signal processing, has been demonstrated to be an effective method for compressing encrypted images with robustness. However, for the exiting CS-based image encryption-then-compression (ETC) systems, image encryption is usually performed by using linear operations. When linear operations are used, we cannot achieve low computational complexity and high security in the meantime. To solve this problem, a novel 2D CS (2DCS) based ETC (2DCS-ETC) scheme is proposed in this paper. First, two nonlinear operations, including global random permutation (GRP) and negative-positive transformation (NPT), are utilized to encrypt the original image for high security purpose. Second, the encrypted image is compressed by using 2DCS for low computational complexity purpose. Furthermore, a gray mapping operation is embedded prior to CS encoding. Since gray mapping strategy can reduce the dynamic range of the CS samples, this strategy is also helpful for the rate distortion (R-D) performance improvement. Third, a 2D projected gradient with embedding decryption (2DPG-ED) algorithm is proposed, which can be utilized for the original image reconstruction even if the encrypted image is not sparse anymore. Compared with the previous CS-based ETC methods, the proposed approach can simultaneously achieve high security and low computational complexity with better robustness.

Due to the emergence of visual privacy issues, encrypted image processing has attracted a lot of attention. Data hiding in encrypted images (DHEI) is an effective method to embed secret data in the encrypted domain and protect the image privacy. However, existing methods do not allow a large amount of embedding capacity. In this letter, we propose a novel DHEI scheme utilizing prediction coding based on compressive sensing (CS) progressive recovery. The original image is pre-processed using CS prediction technique to create spare room, and then the image is encrypted and sent to the data hider. Furthermore, the data hider could embed secret data into Most Significant Bits (MSBs) of the corresponding locations. In the decoding phase, the embedded data can be extracted without any distortion and the original image could be reconstructed. Experimental results demonstrate that our scheme can achieve a higher embedding capacity than other state-of-the-art schemes while keeping a considerable recovered image quality.

As wireless sensor networks (WSN) is resource-constrained and vulnerable, a secure data gathering scheme is desirable. Compressive data gathering based on compressive sensing (CS) embodies its superiority to reduce the communication energy and prolong the network life. However, Compressive data gathering is prone to attack due to the open environment. In this paper, we propose a novel compressive data gathering scheme based on embedding mechanism. To implement privacy-preserving, the selected nodes determined by the sink embed the blinding factors, while the unselected nodes exploit the confusion factors. This scheme ensures low communication overhead at senor nodes and no aggravating complexity at the sink. Extensive evaluations and comparisons validate the effectiveness of our design.

The openness and inclusiveness of Internet of Things (IoT) inevitably result in security risks. Privacy protection in data publishing in IoT has become one of the most important topics. For data owners, besides the privacy of released information, they are increasingly concerned about the privacy of unreleased data because data users are particularly keen to analyze results from the released information. Our experimental results demonstrate that a small number of known data could be successfully utilized to estimate the unknowns. To address the unreleased data privacy while guaranteeing the utility of released data, we propose a privacy-aware controllable compressed data publishing strategy that could resist sparse estimation attacks efficiently. Specifically, by introducing compressive sensing (CS) technology to achieve compressed data publishing, communication overhead is reduced greatly. With the help of CS reconstruction error, the privacy protection capability of unreleased data is enhanced under the premise of the utility of released data. What is more, users are divided into the data user and the authorized user to realize controllability of the data owner. For the same compression data, the data user with general permissions is limited to the released data from the data owner; while the authorized user is entitled to access the whole data. Furthermore, we analyze the upper bound of the released data number that could satisfy the privacy requirement of the unreleased data. The experimental results demonstrate that the utility of the released data, the privacy of the unreleased data, and the compressibility all achieve desirable effect.

A novel method of meaningful image encryption is proposed in this paper. A secret image is encrypted into another meaningful image using the algorithm of reversible data hiding (RDH). High covertness can be ensured during the communication, and the possibility of being attacked of the secret image would be reduced to a very low level. The key innovation of the proposed method is that RDH is applied to compressive sensing (CS) domain, which brings a variety of benefits in terms of image sampling, communication and security. The secret image after preliminary encryption is embedded into the sparse representation coefficients of the host image with the help of the dictionary. The embedding rate could reach 2 bpp, which is significantly higher than those of other state-of-art schemes. In addition, the computational complexity of receiver is reduced. Simulations verify our proposal.

The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.

This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT) and Compressive Sensing (CS). The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, from which the characteristic digest values are generated. Comparing with the existing image authentication algorithms, the proposed scheme can realize not only tamper detection and localization but also tamper recovery. Moreover, tamper recovery is based on block division and the recovery accuracy varies with the contents that are possibly tampered. If either the watermark or low-frequency part is tampered, the recovery accuracy is 100%. The experimental results show that the scheme can not only distinguish the type of tamper and find the tampered blocks but also recover the main information of the original image. With great robustness and security, the scheme can adequately meet the need of secure image transmission under unreliable conditions.

We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits (MSBs) of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

Abstract The preservation of high peak signal‐to‐noise ratio of the watermarked image is required in some applications. The chaos‐based fragile watermarking proposed by Rawat et al. in 2011 can meet this requirement, for only one LSB plane of the original image is modified by the watermark data. However, there are three drawbacks in the original scheme: (i) insecurity, the host image can be tampered without being detected; (ii) the detected tampered area is incomplete; and (iii) the most important thing is that the tampered area is unrecoverable. Enlightened by the work of Rawat et al., we propose a new recoverable chaos‐based fragile watermarking scheme, in which all of the drawbacks in the original scheme have been overcome, while the size of the watermark is unchanged, therefore the high peak signal‐to‐noise ratio characteristic can be preserved. The chaotic map is used to ensure the security; the idea of block dividing is adopted to improve the tamper detection and to make storage space for the recovery data embedding; and the border matching recovery based on vector quantization is proposed to improve the recovery result of the target image. Experiments show that our method is secure and recoverable, and the recovery ability is extremely satisfactory. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Key‐based interval splitting arithmetic coding (KAC) possesses both encryption and compression capabilities. However, it possesses vulnerability to chosen‐plaintext attack because the attacker can explore the relationship between the key and the codeword to deduce the secret key. In order to resist this attack, we propose to introduce perturbation into KAC. The perturbation‐based KAC not only avoids the flaw of KAC that the splitting keys are usually located at the endpoint of certain codeword or at the border of two codewords but also removes the restriction that the keys are only allowed in certain sub‐intervals, which result in great convenience to the key scheduler. In addition, based on generalized arithmetic coding using Generalized Luröth Series, we study the phase‐space splitting of a chaotic map for generalized KAC and suggest the generalized perturbation‐based KAC. This leads to the design of a joint compression and encryption scheme with more powerful cryptographic features. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network.

Body sensor networks (BSNs) and WiFi networks have been widely investigated due to the availability of sensor motes and WiFi devices, but they are commonly deployed separately. In this paper we propose to optimize the total communication energy consumption of BSN and WiFi (BSN-WiFi) networks using joint data rate adaptation. More specifically, we first elaborate the BSN-WiFi network system in four consecutive phases. Then based on the system, we analyze the communication energy consumption, throughput and time delay, and provide a signal-to-noise ratio and packet delivery ratio (SNR-PDR) mappings of BSN and WiFi networks. Next, we build an energy optimization model with constraints of SNR-PDR mappings, throughput, and time delay to minimize the total communication energy consumption in BSN-WiFi networks. With the input of SNR values, we solve this model by cvx to obtain the output of optimal data rates associated with SNR values, which are then tabulated for online data rate adaptation. Finally, we collect 20-minute traces from a specific BSN-WiFi network system for performance evaluation, and the results demonstrate that our optimal data rate solution achieves up to 86% energy savings comparing with the solutions using fixed data rates.

In this paper, we propose to optimize energy consumption in heterogeneous wireless networks through joint packet size optimization. Specifically, we consider a two-hop data communication system composed of a body sensor network (BSN) and a WiFi network. Within the system, we formulate an energy consumption optimization problem with the constraints of both throughput and time delay. Mathematically, we convert this problem into a geometric programming (GP) problem, which is then numerically solved. The solutions can be used by both the BSN and the WiFi network to dynamically change their packets' payload sizes based on their current packet delivery ratios (PDRs). Since the PDRs are time-varying, we tabulate an offline payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed two-hop BSN-WiFi network and simulate the energy consumption. The performance evaluation results show that our solution achieves up to 70% energy savings compared with solutions that use fixed packet sizes.