Existing facial image super-resolution methods have identified the capacity to transform low-resolution facial images into high-resolution ones. However, clearer high-resolution facial images increase the possibility of accurately extracting soft biometric features, such as gender, posing a significant risk of privacy leakage. To address this issue, we propose a gender-protected face super-resolution network, which can incorporate gender-identified privacy information by introducing fine image distortion during the super-resolution process. It progressively transforms low-resolution images into high-resolution ones while partially disturbing the face images. This procedure ensures that the generated super-resolution facial images can still be utilized by face matchers for matching purposes, but are less reliable for attribute classifiers that attempt to extract gender features. Furthermore, we introduce leaping adversarial learning to help the super-resolution network to generate gender-protected facial images and work on arbitrary gender classifiers. Extensive experiments have been conducted using multiple face matchers and gender classifiers to evaluate the effectiveness of the proposed network. The results also demonstrate that our proposed image super-resolution network is adaptable to arbitrary attribute classifiers for protecting gender privacy, while preserving facial image quality.

Image rescaling (IR) seeks to determine the optimal low-resolution (LR) representation of a high-resolution (HR) image to reconstruct a high-quality super-resolution (SR) image. Typically, HR images with resolutions exceeding 2K possess rich information that is unevenly distributed across the image. Traditional image rescaling methods often fall short because they focus solely on the overall scaling rate, ignoring the varying amounts of information in different parts of the image. To address this limitation, we propose a Block-Based Multi-Scale Image Rescaling Framework (BBMR), tailored for IR tasks involving HR images of 2K resolution and higher. BBMR consists of two main components: the Downscaling Module and the Upscaling Module. In the Downscaling Module, the HR image is segmented into sub-blocks of equal size, with each sub-block receiving a dynamically allocated scaling rate while maintaining a constant overall scaling rate. For the Upscaling Module, we introduce the Joint Super-Resolution method (JointSR), which performs SR on these sub-blocks with varying scaling rates and effectively eliminates blocking artifacts. Experimental results demonstrate that BBMR significantly enhances the SR image quality in the of 2K and 4K test dataset compared to initial network image rescaling methods.

Over the past few decades, Internet video streaming has seen explosive growth, pushing network resources to their limits. Video Super-Resolution (VSR) technology, which enhances video quality while reducing bandwidth usage, offers a promising solution to replace traditional video delivery frameworks. However, existing real-world VSR approaches often struggle when faced with inevitable packet loss during network transmission, especially in bandwidth-constrained and low-latency environments. This packet loss introduces amplified noise and artifacts, significantly degrading visual quality. In this work, we decouple the various degrees of degradation caused by packet loss and comprehensively analyze the impact of different types of packet loss. To address these challenges, we propose ReinVSR, an efficient countermeasure strategy that mitigates the detrimental effects of packet loss without introducing additional network overhead. ReinVSR employs a two-pronged approach: a pre-restore module to mitigate missing pixel information and a Local Hidden State Attention module to rectify semantic distortions at the feature level by replacing corrupted hidden states with more accurate representations. Specifically, we leverage neighboring frames to generate a pool of hidden features, which are then refined using a novel spatial attention mechanism to aggregate more authentic and accurate hidden states. Extensive experiments demonstrate that ReinVSR outperforms state-of-the-art methods, achieving significant improvements in visual quality. It offers a robust and effective solution for high-quality video streaming in bandwidth-limited environments.

In certain specific scenarios, there is a risk of privacy leakage in terms of the soft biometric attributes on a person’s face. However, existing face privacy-enhancing techniques suffer from limited generalizability, meaning that they can only induce misclassification in a specific classifier but fail to generalize this effect well to arbitrary attribute classifiers. Moreover, existing methods reverse attributes to improve face privacy, but this may result in privacy recovery. To address those problems, we propose GFPNet, a novel privacy-enhancing model that can provide generalizable and reliable privacy to face images. The key factor for improving generalizability is that GFPNet uses defense training, which is an effective way to improve model robustness, to dynamically strengthen the mediocre auxiliary attribute classifier during iterative training. Specifically, the generalizability of GFPNet is enhanced in the game between attack and defense, where the generator attempts to deceive the auxiliary attribute classifier and the classifier defends against the generator’s attack by defense training. Furthermore, instead of reversing attributes, skewing attributes to one side is used to avoid attribute recovery. GFPNet also integrates a face matcher, multi-scale discriminator, and Demiguise Attack to improve face matching and image quality. Extensive experiments demonstrate that GFPNet has excellent generalizability to arbitrary attribute classifiers and satisfactory face-matching utility.

Real-time data collected from users can help various applications provide services, but there is a risk that sensitive information will be leaked. Existing LDP-based approaches mainly perturb each data point, which severely affects the time-series patterns, leading to sensitive information being leaked out from multiple consecutive significant patterns. In this paper, we focus on dynamic data streams under honest but curious servers and propose a privacy-preserving method called PP-LDP to protect data privacy while preserving data stream patterns and improving the utility of published data. To this end, our approach consists of three main parts. First, we sample the points that can represent the data stream patterns by improving the popular Least Squares Segmented Linear Fit method. Then, we use an adaptive budget allocation method to perturb the sampling points and provide w-event level privacy. Finally, we perform post-processing optimization of the data streams with Kalman filters to further improve the utility of the data streams. Extensive experimental results on realistic datasets show that our proposed scheme can not only protect the data streams’ privacy but also effectively preserve patterns and guarantee the utility of private data.

Distributed data storage (DDS) can assist compressive crowdsensing (CCS) to solve the challenge of temporary data storage in the network. Block compressive sensing effectively addresses DDS of large spatiotemporal data from crowdsensing, allowing efficient reconstruction at low storage and computational costs. However, when the data is unevenly distributed over the sensing area, existing algorithms ignore the variability of information between blocks and still require the central server to uniformly collect samples from each block stored on the mobile device, leading to a reduction in overall reconstruction accuracy. To address this, we propose an adaptive sampling allocation strategy that deeply analyzes the statistical information of each block which can help the central server to collect the number of measurements for each block adaptively to improve the sampling quality. Additionally, we consider the correlation between blocks and use a global denoising strategy to further improve the reconstruction accuracy. Experimental results demonstrate that, compared to the state-of-the-art DDS-CCS algorithm, our proposed adaptive sampling allocation with a joint-denoising mechanism significantly improves the accuracy of the information-rich blocks that most affect the global accuracy, and hence the global reconstruction accuracy. which also remains robust to different block sizes and exhibits improved stability.

Distributed data storage (DDS) in mobile crowdsensing (MCS) systems has recently gained popularity. Data should be briefly saved on participants’ mobile devices before being gathered once the centralized cloud servers resume normal operations. For MCS systems, the existing DDS strategies briefly considered reconstructing the scene as precisely as possible without thinking about the costs of each step. However, our goal is to obtain a sufficiently accurate approximation of the sensing data from mobile participants with as few costs as possible. We note a crucial observation: when a specified number of participants have been transmitted to a central server, the sensing data has already been well reconstructed, and the accuracy advancement with additional transmitted participants is minimal. In our scheme, two stopping criteria are proposed for DDS in compressive MCS, which aims to enhance recovery performance while reducing the costs of the whole process. In the first stopping criterion, we established a rule to stop the continued recruitment of participants. The algorithm adaptively increases the number of participants until the reconstruction accuracy meets the requirement. Another stopping criterion of the reconstruction algorithm is designed to find a more accurate number of iterations than the original. The experiment results demonstrate that the first stopping criterion can reduce participants’ collection while obtaining an approximate value. The second stopping criterion assists the reconstruction algorithm in terminating at a more appropriate number of iterations, saving computing costs while ensuring accuracy.

Sensing data acquired with crowdsensing are generally stored at central cloud servers, since massive data are involved and sensing devices do not have enough space to store them. Although each sensing device only has limited storage capacity, the total size of storage across thousands of devices can be considerable. In view of this, this paper addresses decentralized storage problem in mobile crowdsensing system, providing an alternative to cloud-based data storage. By investigating a virtual sensor model, the movement of a participant in the target sensing area is formulated as a random sampling over the data field related to this area. With a particular encoding algorithm, the data field is compressed into only one measurement along with a random sampling process. Each participant stores its own measurements as if various compressed snapshots of the data field are separately stored by different participants. We further investigate a recovery algorithm, reconstructing the original data field by carefully decoding enough measurements. Extensive experiments validate the proposed storage scheme under various crowdsensing scenarios, and our scheme achieves excellent performance in terms of recruitment overhead, decoding time, and decoding accuracy.

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

In the recent past, human activity recognition research has focused on using WiFi channel state information (CSI) as a viable alternative to legacy systems like video and sensor-based activity recognition having limitations such as privacy invasion, obtrusiveness, and the inconvenience of wearing sensory devices. While the performance of CSI-based activity recognition models is impressive, many of the models are built using offline processed data from regulated settings which hinders their application in real-time. However, real-life human activity recognition requires models to be responsive to identifying activities in real-time. To address the shortcoming of CSI-based activity recognition models, we propose a deep learning object detection framework and instance segmentation for multiple human activity recognition using WiFi signals. The real-time CSI data from the signal is captured on a sliding window and converted into time-frequency domain images of the activity stream using continuous wavelet transform (CWT). Since it is impossible to pre-segment activities within a stream in real-time, the power profile from the transformed images is exploited to provide insights for deep learning instance segmentation to identify each unique human activity. The evaluation is carried out using real-time CSI data with single and multiple human activities. The results show that real-time model classification accuracy is 93.80% on average and instance segmentation accuracy of 90.73%.

A number of image compressive sensing (CS) algorithms were proposed in the past two decades, aiming at yielding recovered images with the best possible visual effect. However, it is quite difficult to further improve the image quality for human eyes. For example, in the low-rate sampling scenarios, CS algorithms always suffer degraded performance and can only recover less visually appealing images. We notice that what human beings concern with is the visual quality of an image, while machine users care much more about its latent metrics, such as recognition accuracy, rather than the subjective visual effect. Inspired by this point, we develop a machine recognition-oriented image CS with an adversarial learning strategy. Some adversarial models are investigated to make the recognition accuracy as an additional optimization goal of the CS reconstruction network. Through end-to-end training, CS reconstruction network automatically learns an image recognition pattern, and produce recovered images owning extra recognition metric, which makes them become more suited for machine users. Experimental results indicate that the images recovered with the proposed adversarial learning strategy can be recognized with significantly higher accuracy compared to that with the existing CS algorithms.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

Privacy concerns over face recognition systems have attracted extensive attention in various fields. For gender privacy-preserving work, there are two key challenges: 1)privacy, i.e., confusing gender classifiers and 2)utility, i.e., maintaining its face verification performance. To address both issues, this article develops a novel gender-adversarial network, referred to as Gender-AN, to impart gender privacy to face images. Gender-AN employs an attribute-independent encoder–decoder GAN-based network to perturb the input face image, training with the assistance of the proper facial attributes. The perturbed image is then able to obfuscate gender classifiers while maintaining identity discriminability. To optimize the generator, a multitask-based loss function is utilized, which includes attribute manipulation loss, face matcher loss, adversarial loss, and reconstruction loss functions. This optimization facilitates our model to achieve the generalization, verification preserve, and natural appearance, simultaneously. Extensive experiments confirm the effectiveness of the proposed model in enhancing gender privacy and preserving face verification utility.

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at https://github.com/siwangzhou/MCS-Storage .

Enabling contactless and device-free hands tracking on mobile device leads to new user interaction experiences. In this article, we propose CTrack, a device-free and collaborative hands motion tracking solution for above-device interaction by using acoustic signals. CTrack does not require instrumenting hands with sensors. We achieve this by transforming the device into an active sonar system that transmits inaudible sound signals and tracks the echoes of the hand at its microphones. To guarantee subcentimeter-level tracking accuracies, we present an adaptive approach that uses the chirp’s time of flight to accurately measure the distance from the hand to an in-built speaker array. Then, the hand, speaker array, and microphone array yield a set of different ellipses. The hand position can be pinpointed exactly by solving and optimizing the intersection of these ellipses. Our evaluation shows that CTrack can achieve 2-D motion tracking with an average accuracy of 14 mm using the in-built microphones and speakers of a Nexus 6P.

Existing WiFi recognition schemes are capable of discovering patterns of indoor semantics, such as human activity, identity, indoor environment, and so on. We note that channel state information (CSI) presents an opportunity for hackers to learn indoor privacy, however, currently there is a lack of security research on CSI. In this article, we are the first to discuss and define the security problem of CSI signals, which is further extended to the problems of nontargeted protection and targeted protection. To solve them, we present two types of adversarial autoencoder networks (AAENs). Through replacing the original signals with the generated adversarial ones, the protected semantic features are modified, and the significant features of the other semantics required to be recognized are reserved. Intensive evaluations demonstrate that with the proposed AAENs, the recognition accuracy of the protected semantic can be significantly decreased, while still maintaining the other semantics to be identified correctly.

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network-based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full-image removal of blocking artifacts. Specifically, with our multi-channel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block-based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available athttps://github.com/siwangzhou/DeepBCS.

Channel state information-based activity recognition has gathered immense attention over recent years. Many existing works achieved desirable performance in various applications, including healthcare, security, and Internet of Things, with different machine learning algorithms. However, they usually fail to consider the availability of enough samples to be trained. Besides, many applications only focus on the scenario where only single subject presents. To address these challenges, in this paper, we propose a three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment. Different system phases are applied according to the size of available collected samples. Specifically, distance-based classification using dynamic time warping is applied when there are few samples in the profile. Then, support vector machine is employed when representative features can be extracted from training samples. Lastly, recurrent neural networks is exploited when a large number of samples are available. Extensive experiments results show that Wi-multi achieves an accuracy of 96.1% on average. It is also able to achieve a desirable tradeoff between accuracy and efficiency in different phases.

Existing work on distributed networked storage, although extensive, has generally focused on the recovery of global data field covering the entire network. This, while demanded by a broad range of applications, has ignored cases where only a subset of the data are needed, for example, from a local region of the network. Based on this observation and the fact that the sensor readings are correlated, this paper proposes a compressive networked storage solution. Specifically, by employing the compressive sensing (CS) theory, we present a lazy-encoding algorithm with local dissemination and a region-based reconstruction algorithm. Utilizing our local dissemination strategy, sensor readings only have to be disseminated and stored in their respective regions, which makes the dissemination cost decrease significantly. With the lazy-encoding algorithm, the readings in specified local regions are capable of being encoded individually, dramatically reducing the decoding ratio. The region-based reconstruction algorithm is introduced to explore the inter-region correlation, aiming at offering improved data accuracy. We further provide the mathematical foundation that our reconstruction algorithm could ensure efficient CS recovery. Experimental results using real sensor readings show that the proposed scheme is especially beneficial to the recovery of local data. At the same time, our scheme can recover the global data field as well without increasing reconstruction error.

We investigate the problem of distributed networked storage with compressive sensing in wireless sensor networks, and a compressive storage scheme for local data query is proposed. Specifically, we propose a simple but efficient one-step data dissemination strategy, and the dissemination cost is reduced dramatically. We further present a lazy-encoding algorithm, using which the local data are capable of being reconstructed without recovering the global data field if not necessary. Thus the decoding ratio decreases significantly. Experiments using real sensor data show that the proposed scheme achieves far better local data recovery performance compared to the existing ones.

We present the first WiFi based driver state recognition system: SafeDrive-Fi. Our proposed framework extracts fine-grain Channel State Information (CSI) of WiFi signal to accurately predict driver states through gestures and body movements. Different from vision based techniques, SafeDrive-Fi provides a simple, cost-effective and ubiquitous solution to prevent accidents and loss of lives due to reckless driving. We incorporate a unique DETECT algorithm to differentiate between normal and dangerous driving in a challenging and noisy in-vehicle conditions. Using only commercially available products, SafeDrive-Fi is compatible with 802.11n/ac and can assist drivers and law enforcement in discovering dangerous driving states. To the best of our knowledge, this is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions. SafeDrive-Fi achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal Strength (RSS) based solution using an already deployed infrastructure.

Block based compressed sensing (BCS) is a novel framework in image signals sampling and recovery due to its advantages in terms of both low sampling burden and lightweight recovery complexity. In this paper, we propose a novel asymmetric BCS scheme to further improve the image recovery accuracy. In the sampling process, image blocks are partitioned into smaller sub-blocks, and those small sub-blocks are used to allocate sampling resources. In the recovery process, the small sub-blocks with similar feature information are assembled into virtual blocks with larger size, and the corresponding transforming coefficients are then more compressible. The proposed scheme improves the recovered images from the fairer resources allocation and much greater compressibility. The experimental results demonstrate that, compared to the existing BCS approaches, our proposed scheme has higher recovery quality, without increasing sampling and recovery complexity.

The latest research progress of the theory of compressed sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data in WSNs. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of gathered data. Simulation results show that the proposed scheme improves the reconstruction quality compared to the existing ones.

Collaborative representation classification (CRC) has attracted increasing attention in face recognition (FR) tasks. The two-phase sparse representation (TPSR) methods are the improved schemes. However, most TPSR methods decrease training samples in the first step, resulting in less similarities or discrimination for representation, even unstable classification. In this paper, we propose a new two-phase representation based FR approach with random-filtering virtual samples, called Random-Filtering based Sparse Representation (RFSR) scheme. To increase the similarity in the same class and the discrimination between different classes, RFSR first uses original training samples and their corresponding random-filtering virtual samples to constructs a new training set. Then it exploits the new training set to perform CRC. The experiment results indicate that our method outperforms the two-phase test sample sparse representation (TPTSSR) method and the simple and fast representation-based (SFRB) scheme.

An extensive set of research efforts have explored Channel State Information for human activity detection. By extracting CSI from a sequence of packets, one can statistically analyze the temporal variations embedded therein and recognize corresponding human activities. In this paper, we present Wi-Chase, a sensorless system based on CSI from ubiquitous WiFi packets for human activity detection. Different from existing schemes utilizing only CSI of one or a small subset of subcarriers, Wi-Chase fully utilizes all available subcarriers of the WiFi signal and incorporates variations in both their phases and magnitudes. As each subcarrier carries integral information that will improve the recognition accuracy because of detailed correlated information content in different subcarriers, we can achieve much higher detection accuracy. To the best of our knowledge, this is the first system that gathers information from all the subcarriers to identify and classify multiple activities. Our experimental results show that Wi-Chase is robust and achieves an average classification accuracy greater than 97% for multiple communication links.

With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with privacy preserving ranked multi-keyword search in a multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel additive order and privacy preserving function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation. Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

With the advent of cloud computing, it becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve these data. For privacy concerns, secure searches over encrypted cloud data motivated several researches under the single owner model. However, most cloud servers in practice do not just serve one owner, instead, they support multiple owners to share the benefits brought by cloud servers. In this paper, we propose schemes to deal with secure ranked multi-keyword search in a multi-owner model. To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel Additive Order and Privacy Preserving Function family. Extensive experiments on real-world datasets confirm the efficacy and efficiency of our proposed schemes.