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Due to the integration and utilization of the parasitic capacitance of the transformer, the LCC resonant converters with a capacitive output filter are widely used in high-voltage power supplies for microwave generation, X-ray, ion implantation, and other fields. However, it is challenging for the LCC to maintain high efficiency in a wide output voltage range. In this article, an optimal control coordinate trajectories (OCCTs)-based phase-shift pulse-frequency modulation (PS-PFM) control method is proposed to achieve high efficiency by reducing the switching frequency range and the turn-off current. With the normalized time-domain analysis, the operation stage trajectories of LCC and their boundary conditions under the PS-PFM control are derived. Based on this, the OCCTs of the switching frequency and the duty cycle at different voltage gains are precisely calculated. By associating the duty cycle with the switching frequency and the output voltage gain through a fit equation based on the OCCTs data, the proposed PS-PFM control strategy can adaptively adjust the optimal duty cycle, simplifying one of the control freedom degrees. Then, an optimization design algorithm for the resonant network parameter is proposed to reduce the conduction loss of LCC. Finally, a 10–20-kV/15-kW LCC resonant high-voltage power supply prototype is built to verify the feasibility of the proposed control strategy.

Monitoring Chlorophyll-a (Chl-a) concentration in ocean is of considerable significance for early warning of algae disasters, marine ecological environment protection, etc. However, due to various uncontrollable factors such as cloud cover and thick aerosols, the missing rate of observed Chl-a data is quite high, which seriously hinders its applications. The existing data imputation methods are mostly applicable to Chl-a data with a low missing rate, and perform much worse when the missing rate reaches 0.7 or above. To address this issue, we proposed a self-supervised spatiotemporal imputation (S2-STI) model for highly sparse Chl-a data imputation by fusing multisource satellite data. First, to obtain comprehensive information about the missed Chl-a data, we designed a multisource sparse data fusion (MSDF) module to fuse multisource satellite data, including Chl-a data, sea surface temperature (SST) data, and photosynthetically available radiation data. MSDF constructs a spatialtemporal graph network to learn high-quality spatiotemporal representations of SST and photosynthetically active radiation (PAR) data, and fuses the learned representations with Chl-a data to enrich the information for imputation. Then, we developed a generation-based data imputation (GDI) module to model the distribution of Chl-a data based on the outputs of MSDF. Considering the high data sparsity, we designed a self-supervised training strategy to train S2-STI in the absence of ground truth. Finally, we leverage the data generated by the GDI module in the trained S2-STI model to fill in missing values in the sparse Chl-a data. Experiments on real datasets show that S2-STI achieves much better performance than the existing data imputation methods. Specifically, for the Chl-a data with a missing rate of 0.9, S2-STI improves by at least 12% in terms of masked autoencoder (MAE) error when compared with the strong baseline methods.

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteinsthat contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

Marine plankton classification is important for monitoring marine biological populations and understanding marine ecosystems. However, it is difficult to obtain abundant annotated image data of marine plankton to train classification models of high quality. To classify marine plankton with a small number of image samples, a cross‐domain few‐shot learning model (CDFM) is proposed. First, CDFM learns knowledge from existing labelled images and then transfers the knowledge to new images. In this process, there is an issue of domain differences between the new images and the existing datasets due to differences in data acquisition time and locations. To address this issue, the authors pre‐train a model in the source domain and then use fine‐tuning to adapt it to the target domain. Second, the graph neural network is used as a meta‐learning module to learn a feature distance metric for marine plankton classification with limited samples. Extensive experiments on four marine plankton image datasets are conducted, including Kaggle Plankton, miniPPlankton, ZooScan and WHOI, and CDFM outperforms existing methods for marine plankton classification.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children with complex onset symptoms, and brain function analysis is a helpful way to assist in diagnosis. Functional magnetic resonance imaging (fMRI) studies have revealed that the brain function activity pattern in the resting state is also dynamically changing, that is, there is a meta-stable state transition within the period of acquisition time, which is related to neurological diseases. However, existing studies neglect to mine such state transition pattern information for better disease identification. Here, we propose an end-to-end method that learns meta-stable state transition representation for ADHD identification. First, the fMRI data acquired during the entire period are divided into overlapping segments based on the sliding window operation, and each segment is used to capture the meta-stable state of brain function. Then, for each meta-stable state, a 2-D convolutional neural network (CNN) is adopted to extract functional connectivity (FC) information from FC network (FCN) constructed by fMRI volumes, while a 3-D CNN is used to extract spatial information from fMRI volumes and a bidirectional long short-term memory (Bi-LSTM) is used to extract temporal information from the volume sequence, and the FC feature and spatiotemporal feature are fused to represent the meta-stable state. Finally, a Bi-LSTM with attention mechanism is conducted to learn the state transition pattern from the representations of the meta-stable states, and the state transition feature is used for classification. The results on the ADHD-200 dataset indicate that: 1) the meta-stable transition pattern is indeed useful and can improve identification performance; 2) fusing the static FC feature and dynamic volume sequence feature of brain function can better characterize meta-stable states; and 3) the combination of window size = 50 and step size = 40 is an ideal choice in the sliding window operation, which can obtain the best performance.

With the development of biomedical techniques in the past decades, causal gene identification has become one of the most promising applications in human genome-based business, which can help doctors to evaluate the risk of certain genetic diseases and provide further treatment recommendations for potential patients. When no controlled experiments can be applied, machine learning techniques like causal inference-based methods are generally used to identify causal genes. Unfortunately, most of the existing methods detect disease-related genes by ranking-based strategies or feature selection techniques, which generally return a superset of the corresponding real causal genes. There are also some causal inference-based methods that can identify a part of real causal genes from those supersets, but they are just able to return a few causal genes. This is contrary to our knowledge, as many results from controlled experiments have demonstrated that a certain disease, especially cancer, is usually related to dozens or hundreds of genes. In this work, we present an effective approach for identifying causal genes from gene expression data by using a new search strategy based on non-linear regression-based independence tests, which is able to greatly reduce the search space, and simultaneously establish the causal relationships from the candidate genes to the disease variable. Extensive experiments on real-world cancer datasets show that our method is superior to the existing causal inference-based methods in three aspects: 1) our method can identify dozens of causal genes, and $1/3 \sim 1/2$1/3∼1/2 of the discovered causal genes can be verified by existing works that they are really directly related to the corresponding disease; 2) The discovered causal genes are able to distinguish the status or disease subtype of the target patient; 3) Most of the discovered causal genes are closely relevant to the disease variable.

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.

Document clustering is an important tool for document collection organization and browsing. In real applications, some limited knowledge about cluster membership of a small number of documents is often available, such as some pairs of documents belonging to the same cluster. This kind of prior knowledge can be served as constraints for the clustering process. We integrate the constraints into the trace formulation of the sum of square Euclidean distance function of K-means. Then, the combined criterion function is transformed into trace maximization, which is further optimized by eigen-decomposition. Our experimental evaluation shows that the proposed semi-supervised clustering method can achieve better performance, compared to three existing methods. Keywords: Document clustering Semi-supervised learning Spectral relaxation Clustering with prior knowledge

In random-turn games, players toss a coin to decide who moves. This article studies the complexities of the algorithms for playing random-turn connection games perfectly on regular tessellations. Our study theoretically shows that there are algorithms playing random-turn hex, square, and triangle perfectly in $O(n^9\cdot 2.618^n)$, $O(n^9\cdot 2.746^n)$, and $O(n^9\cdot 3.645^n)$ time for each move, respectively, where $n$ is the board size. We then implement the perfect-playing algorithm for the random-turn square and measure the actual running time it costs for each move. We then compute and analyze the game lengths on random-turn square, hex, and triangle and conjecture that the asymptotic complexity of their game lengths are the same. We finally compare the perfect-playing algorithm with the sampling algorithm by competing against each other, and the numbers of their wins and losses are reported.

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains(TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.orgas an open-source software.

Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD), which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI). Methods: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve the performance of classifying individuals. Experiments are performed on healthy controls and subjects at the early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data. Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at the early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction. Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficiently.

Background: Previous studies have revealed that DNA methylation plays a crucial role in eukaryotic growth and development via involvement in the regulation of gene expression and chromosomal instability. With the advancement of biotechnology, next-generation sequencing (NGS) is emerging as a popular method to explore the functions of DNA methylation, and an increasing number of genome-scale DNA methylation datasets have been published. Several DNA methylation databases, including MethDB, NGSmethDB and MENT have been developed for storing and analyzing the DNA methylation data. However, no public resource dedicated to DNA methylation of Oryza sativa is available to date. Methods & Results: We built a comprehensive database (RMDB) for integration and analysis of DNA methylation data of Oryza sativa. A couple of functional modules were developed to identify the connections between DNA methylation and phenotypes. Moreover, rich graphical visualization tools were employed to facilitate data presentation and interpretation. Conclusion: RMDB is an integrated database dedicated to rice DNA methylation. To the best of our knowledge, this is the first integrated rice DNA methylation database. We believe that RMDB will be helpful to understand the epigenetic mechanisms of Oryza sativa. RMDB is freely available at http://admis.fudan.edu.cn/rmdb.

Background: Enhancers are short DNA regions that improve transcription efficiency by recruiting transcription factors. Identifying enhancer regions is important to understand the process of gene expression. As enhancers are independent of their distances and orientations to the target genes, it is difficult to locate enhancers accurately. Recently, with the development of highthroughput ChIP-seq (Chromatin Immunoprecipitation sequencing) technologies, several computational methods were developed to predict enhancers. However, most of these methods rely on p300 binding sites and/or DNase I hypersensitive sites (DHSs) for selecting positive training samples, which is imprecise and subsequently leads to unsatisfactory prediction performance. Besides, in the literature, there is no work that predicts enhancers from tissues across different developmental stages. Methods: In this paper, we proposed a method based on support vector machines (SVMs) to investigate enhancer prediction on cell lines and tissues from EnhancerAtlas. Specifically, we focused on predicting enhancers on different developmental stages of heart and lung tissues. Results and Conclusion: Our results show that 1) the proposed method achieves good performance on most cell lines and tissues, especially it outperforms several state of the art methods on heart and lung. 2) It is easier to predict enhancers from tissues of adult stage than from tissues of fetal stage, which is proven on both heart and lung tissues.

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains(TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.orgas an open-source software.

Background: Due to the intrinsic compensatory mechanism and cross-talks mong cellular signaling pathways, single-target drugs often fail to inhibit the survival pathways in cancer cells. Some multi-target combination drugs have demonstrated their high sensitivities and low side effects in cancer therapies, and thus drawn intensive attentions from researchers and pharmaceutical enterprises. Method: Although a few computational methods have been developed to infer combination drug sensitivities based on drug-kinase interactions, they either depend on the binarization of drug-kinase binding affinities, which would lead to the loss of weak drug-target inhibitions known to affect significantly the anticancer effects, or disregard the functional group structure among the kinases involved in cancer signalling pathways. In this paper, we employed a sparse linear model, uncertain group sparse representation (UGSR), to infer essential kinases governing the cellular responses to drug treatments in cancer cells, based on the massively collected drug-kinase interactions and drug sensitivity datasets over hundreds of cancer cell lines. The inferred essential kinases can be subsequently used to calculate the cancer cell sensitivities to combination drugs. Results: The leave-one-out cross validations and two real cases show that our method achieve high performance in predict drug sensitivities of combination drugs. Moreover, a user-friendly web interface with interactive network viewer, tabular viewer and other graphical visualization plugins, has been implemented to facilitate data access and interpretation.

The identification of protein complexes is significant to understand the mechanisms of cellular processes. Up to present, many methods on protein complex detection have been developed in static PPI networks. However, static PPI networks cannot accurately describe the behaviours of proteins in the different stages of life cycle of a cell. In this paper, we combine different data sets including gene expression data, GO terms and high-throughput PPI data to reconstruct weighted dynamic PPI networks, on which a new method called CPredictor4.0 are proposed. Specifically, we first calculate protein active probability and protein functional similarity to construct weighted dynamic PPI networks, then define a high-order topological overlap measure of similarity to extract protein complexes based on the core-attachment model. In our experiments, four PPI datasets are used to detect protein complexes. Experimental results indicate that CPredictor4.0 is superior to the existing methods in overall.

In the past decades, advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation. Recently, nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data as well as to interpret them, and has been applied to various fields of biological research. In this paper, we present CloudNMF, a distributed open-source implementation of NMF on a MapReduce framework. Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data, which may enable various kinds of a high-throughput biological data analysis in the cloud. CloudNMF is freely accessible at http://admis.fudan.edu.cn/projects/CloudNMF.html.

Topological analysis of protein-protein interaction (PPI) networks has been widely applied to the investigation on cancer mechanisms. However, there is still a debate on whether cancer proteins exhibit more topological centrality compared to the other proteins in the human PPI network. To resolve this debate, we first identified four sets of human proteins, and then mapped these proteins into the yeast PPI network by homologous genes. Finally, we compared these proteins’ properties in human and yeast PPI networks. Experiments over two real datasets demonstrated that cancer proteins tend to have higher degree and smaller clustering coefficient than non-cancer proteins. Experimental results also validated that cancer proteins have larger betweenness centrality compared to the other proteins on the STRING dataset. However, on the BioGRID dataset, the average betweenness centrality of cancer proteins is larger than that of disease and control proteins, but smaller than that of essential proteins.

MicroRNAs (miRNAs), a class of ∼20–24 nt long non-coding RNAs, have critical roles in diverse biological processes including development, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miRNA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods.

Protein structure prediction is an important but far from being well-resolved problem in computational biology. It is generally regarded that the native structures of proteins correspond to minimum energy states. Potential functions are useful in protein structure prediction. To obtain the optimal parameters of protein potential functions, we introduced several strategies to improve the basic Genetic Algorithm (GA). The improved GA was employed in statistical potential function design and protein structure prediction, and experimental results validate the effectiveness and efficiency of the proposed algorithm.

The prediction of protein function is a difficult and important problem in computational biology. In this study, an efficient method is presented to predict protein function with sequence composition information. Four kinds of basic building blocks of protein sequences are investigated, including N-grams, binary profiles, PFAM domains and InterPro domains. The protein sequences are mapped into high-dimensional vectors by using the occurrence frequencies of each kind of building blocks. The resulting vectors are then taken as input to support vector machine to predict their function based on gene ontology. Experiments are conducted over the subset of GOA database. The experimental results show that the protein function can be predicted from primary sequence information. The method based on InterPro domains outperforms the other building blocks, and gets an overall accuracy of 0.87 and ROC score is 0.93. We also demonstrate that the use of feature extraction algorithms such as latent semantic analysis and nonnegative matrix factorization, can efficiently remove noise and improve the prediction efficiency without significantly degrading the performance. The results obtained here are helpful for the prediction of protein function by using only sequence information.

MicroRNAs (miRNAs) have been recently emerged as a novel class of endogenous post-transcriptional regulators in a variety of animal and plant species. Identifying bona fide miRNA-mRNA interactions is an important but challenging task for our insight into the regulatory mechanism of miRNAs. In this paper, we employ a variant of affinity propagation algorithm customised for bipartite graph to reveal the miRNA-mRNA interactions supported by microarray data. Our extensive experiments on human data sets show that our method performs effectively in screening the miRNA-mRNA interactions predicted by sequence-based approaches to reduce the number of candidate miRNA targets using microarray data.

Short text classification (STC) has attracted increasing interest recently with the rapid growth of Web and social media data existing in short text form. It is a more challenging task than traditional text classification (TC) because of the feature sparsity of the processed short texts, which makes the state of the art TC approaches perform poorly on short texts if being applied straightforwardly. Existing STC approaches deal with the sparse problem mainly by enriching text content with outer corpora or additional information. Though better performance can be obtained, the performance heavily relies on the amount and quality of outer or additional information. What is worse, such outer or additional information is not always available, not to mention the high cost for acquiring such information. In this paper, we introduce a structured sparse representation classifier to effectively classify short texts, and develop an effective approach called convex hull vertices selection to reduce data correlation and redundancy of the dictionary (the set of training texts), which thus substantially boosts STC efficiency and performance. To the best of our knowledge, this is the first work that exploits structured sparsity for STC. Experiments over five datasets show that the proposed approach outperforms the state of the art TC methods in classification effectiveness and the traditional SR classifier in both classification effectiveness and classification efficiency. Furthermore, we carry out an experiment to classify short texts expanded by additional content, which indirectly shows that our approach performs better than the existing STC methods that exploit external text sources. [ABSTRACT FROM AUTHOR]

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X → Y , and (2) no disturbance variable is contained in X , i.e., removing any component of X will weaken the causal relationship between X and Y , then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets. [ABSTRACT FROM AUTHOR]

• A novel privacy preserving method for data publication is proposed based on conditional probability distribution and machine learning techniques, which can offer stronger privacy guarantee and retain better data utility. • A basic algorithm and a complete algorithm are designed respectively for the settings of single sensitive attribute and and multiple sensitive attributes. • An improved complete algorithm is developed using Gibbs sampling, in order to advance the data utility when data are not sufficient. • Both theoretical analysis and experimental results are given, which validate the proposed method. Privacy has received increasing concerns in publication of datasets that contain sensitive information. Preventing privacy disclosure and providing useful information to legitimate users for data mining are conflicting goals. Generalization and randomized response methods were proposed in database community to tackle this problem. However, both of them have postulated the same prior belief for all transactions, which might be wrong modeling and lead to privacy breach. Besides, generalization and randomized response methods usually require a privacy controlling parameter to control the tradeoff between privacy and data quality, which may put the data publishers in a dilemma. In this paper, a novel privacy preserving method for data publication is proposed based on conditional probability distribution and machine learning techniques, which can achieve different prior beliefs for different transactions. A basic cross sampling algorithm and a complete cross sampling algorithm are designed respectively for the settings of single sensitive attribute and multiple sensitive attributes, and an improved complete algorithm is developed by using Gibbs sampling, in order to enhance data utility when data are not sufficient. Our method can offer stronger privacy guarantee, while, as shown in the extensive experiments, retaining better data utility. [ABSTRACT FROM AUTHOR]

Highlights • A novel framework is proposed to provide comprehensive privacy preserving for query processing in information networks, including data privacy, storage privacy and query privacy. • A basic implementation scheme for supporting key-value search under this framework is devised, where search key and commutative encryption are carefully designed for guaranteeing the security of the framework. • A more efficient implementation scheme is developed based on a tradeoff mechanism between security and efficiency, for efficient query processing over large amounts of data. • Both theoretical analysis and experimental results are given, which validate the proposed framework and the implementation schemes. Abstract This paper addresses the privacy issue for query processing in information networks, where users search and aggregate information from many data sources. To cater for the privacy requirements of all parties, we propose a comprehensive privacy preserving framework (CPP in short) for preserving data privacy, query privacy and storage privacy simultaneously, and implement this framework for key-value query processing. We first develop a baseline scheme that adopts commutative encryption with full indistinguishability guarantee. To speedup query processing, we then propose a tradeoff between security and efficiency, which leads to a scheme that significantly reduces the use of commutative encryption with a little and bounded security cost. Finally, we validate the proposed framework and the implementation schemes by both theoretical analysis and experimental results. [ABSTRACT FROM AUTHOR]