IEEE Journal of Selected Topics in Signal Processing Impact Factor & Information

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers

Current impact factor: 2.37

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 2.373
2013 Impact Factor 3.629
2012 Impact Factor 3.297
2011 Impact Factor 2.88
2010 Impact Factor 2.571
2009 Impact Factor 1.2

Impact factor over time

Impact factor

Additional details

5-year impact 3.68
Cited half-life 4.60
Immediacy index 0.38
Eigenfactor 0.01
Article influence 1.87
Other titles IEEE journal of selected topics in signal processing, Selected topics in signal processing
ISSN 1932-4553
OCLC 158906070
Material type Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Institute of Electrical and Electronics Engineers

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  • Classification

Publications in this journal

  • IEEE Journal of Selected Topics in Signal Processing 12/2015; 9(8):1363-1365. DOI:10.1109/JSTSP.2015.2497458
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    ABSTRACT: Physiological and biophysical models have been proposed to link neuronal activity to the Blood Oxygen Level-Dependent (BOLD) signal in functional MRI (fMRI). Those models rely on a set of parameter values that cannot always be extracted from the literature. In some applications, interesting insight into the brain physiology or physiopathology can be gained from an estimation of the model parameters from measured BOLD signals. This estimation is challenging because there are more than 10 potentially interesting parameters involved in nonlinear equations and whose interactions may result in identifiability issues. However, the availability of statistical prior knowledge about these parameters can greatly simplify the estimation task. In this work we focus on the extended Balloon model and propose the estimation of 15 parameters using two stochastic approaches: an Evolutionary Computation global search method called Differential Evolution (DE) and a Markov Chain Monte Carlo version of DE. To combine both the ability to escape local optima and to incorporate prior knowledge, we derive the target function from Bayesian modeling. The general behavior of these algorithms is analyzed and compared with the de facto standard Expectation Maximization Gauss-Newton (EM/GN) approach, providing very promising results on challenging real and synthetic fMRI data sets involving rats with epileptic activity. These stochastic optimizers provided a better performance than EM/GN in terms of distance to the ground truth in 4 out of 6 synthetic data sets and a better signal fitting in 12 out of 12 real data sets. Non-parametric statistical tests showed the existence of statistically significant differences between the real data results obtained by DE and EM/GN. Finally, the estimates obtained from DE for these parameters seem both more realistic and more stable or at least as stable across sessions as the estimates from EM/GN.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; DOI:10.1109/JSTSP.2015.2502553
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    ABSTRACT: E-healthcare systems have been increasingly facilitating health condition monitoring, disease modeling and early intervention, and evidence-based medical treatment by medical text mining and image feature extraction. Owing to the resource constraint of wearable mobile devices, it is required to outsource the frequently collected personal health information (PHI) into the cloud. Unfortunately, delegating both storage and computation to the untrusted entity would bring a series of security and privacy issues. The existing work mainly focused on fine-grained privacy-preserving static medical text access and analysis, which can hardly afford the dynamic health condition fluctuation and medical image analysis. In this paper, a secure and efficient privacy-preserving dynamic medical text mining and image feature extraction scheme PPDM in cloud-assisted e-healthcare systems is proposed. Firstly, an efficient privacy-preserving fully homomorphic data aggregation is proposed, which serves the basis for our proposed PPDM. Then, an outsourced disease modeling and early intervention is achieved, respectively by devising an efficient privacy-preserving function correlation matching PPDM1 from dynamic medical text mining and designing a privacy-preserving medical image feature extraction PPDM2. Finally, the formal security proof and extensive performance evaluation demonstrate our proposed PPDM achieves a higher security level (i.e., information-theoretic security for input privacy and adaptive chosen ciphertext attack (CCA2) security for output privacy) in the honest but curious model with optimized efficiency advantage over the state-of-the-art in terms of both computational and communication overhead.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2427113
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    ABSTRACT: Shamir's $(n,k)$ threshold secret sharing is an important component of several cryptographic protocols, such as those for secure multiparty-computation and key management. These protocols typically assume the presence of direct communication links from the dealer to all participants, in which case the dealer can directly pass the shares of the secret to each participant. In this paper, we consider the problem of secret sharing when the dealer does not have direct communication links to all the participants, and instead, the dealer and the participants form a general network. Existing methods are based on separate secure message transmissions from the dealer to each participant, requiring considerable coordination and communication in the network. We present a distributed algorithm for disseminating shares over a network, which we call the “SNEAK” algorithm, requiring each node to know only the identities of its one-hop neighbors. While SNEAK imposes a stronger condition on the network by requiring the dealer to be what we call $k$-propagating rather than $k$-connected as required by the existing solutions, we show that in addition to being distributed, it achieves significant reduction in the amount of communication and the randomness required. We also derive information-theoretic lower bounds on the amount of communication for secret sharing over networks, which may be of independent interest.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2422682
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    ABSTRACT: Adding Laplacian noise is a standard approach in differential privacy to sanitize numerical data before releasing it. In this paper, we propose an alternative noise adding mechanism: the staircase mechanism, which is a geometric mixture of uniform random variables. The staircase mechanism can replace the Laplace mechanism in each instance in the literature and for the same level of differential privacy, the performance in each instance improves; the improvement is particularly stark in medium-low privacy regimes. We show that the staircase mechanism is the optimal noise adding mechanism in a universal context, subject to a conjectured technical lemma (which we also prove to be true for one and two dimensional data).
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2425831
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    ABSTRACT: A robust and secure database for spectrum sharing in cognitive radio networks that is obscured from the viewpoint of secondary users is presented. The database allocations secure features of white space resource usage from being learned. The design of non-inferable database is based on two cases. In the first case, the primary or spectrum lender has no knowledge of secondary users or potential jammers among them. In this case, the problem is modeled as a Markov decision process. In the second case, the primary system has some knowledge about spectrum borrowers and the problem is modeled as a Bayesian Stackelberg game. Mutual information interpretation of the Bayesian Stackelberg game is also presented. The solutions facilitate releasing more bandwidth as required by US National Broadband Plan. Applications of this scheme are manifold. This design can be used for securing spectrum resources, for example radar white spaces, while being shared with LTE and commercial communication systems. Further, it provides jammer-proof spectrum sharing among various communication, detection, and navigation systems. Simulation results verify this scheme improves system throughput while maintaining desired obfuscation level or entropy.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2426132
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    ABSTRACT: The articles in this special section is designed to provide a venue for state-of-the-art research being doing in how signal and information processing is advancing the field of information privacy.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1173-1175. DOI:10.1109/JSTSP.2015.2462391
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    ABSTRACT: In this paper we consider the problem of achieving a positive error-free communications rate without being detected by an eavesdropper—we coin this the privacy rate. Specifically, we analyze the privacy rate over additive white Gaussian Noise (AWGN) channels with finite and infinite number of samples and Rayleigh single input-single (SISO) and multiple input-multiple output (MIMO) channels with infinite samples when an eavesdropper employs a radiometer detector and has uncertainty about his noise variance. Leveraging recent results on the phenomenon of a signal-to-noise ratio (SNR) wall when there is eavesdropper noise power measurement uncertainty, we show that a nonzero privacy rate is possible. We also show that in this scenario, the detector should not necessarily take as many samples as possible.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2421477
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    ABSTRACT: Recent increase in online privacy concerns prompts the following question: can a recommender system be accurate if users do not entrust it with their private data? To answer this, we study the problem of learning item-clusters under local differential privacy, a powerful, formal notion of data privacy. We develop bounds on the sample-complexity of learning item-clusters from privatized user inputs. Significantly, our results identify a sample-complexity separation between learning in an information-rich and an information-scarce regime, thereby highlighting the interaction between privacy and the amount of information (ratings) available to each user. In the information-rich regime, where each user rates at least a constant fraction of items, a spectral clustering approach is shown to achieve a sample-complexity lower bound derived from a simple information-theoretic argument based on Fano's inequality. However, the information-scarce regime, where each user rates only a vanishing fraction of items, is found to require a fundamentally different approach both for lower bounds and algorithms. To this end, we develop new techniques for bounding mutual information under a notion of channel-mismatch. These techniques may be of broader interest, and we illustrate this by applying them to (i) learning based on 1-bit sketches, and (ii) adaptive learning. Finally, we propose a new algorithm, MaxSense, and show that it achieves optimal sample-complexity in the information-scarce regime.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2423254
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    ABSTRACT: It is advantageous for collecting agents in interconnected systems to exchange information (e.g., functions of their measurements) in order to improve their local processing (e.g., state estimation) because of the typically correlated nature of the data in such systems. However, privacy concerns may limit or prevent this exchange leading to a tradeoff between state estimation fidelity and privacy (referred to as competitive privacy). This paper focuses on a two-agent interactive setting and uses a communication protocol in which each agent is capable of sharing a compressed function of its data. The objective of this paper is to study centralized and decentralized mechanisms that can enable and sustain non-zero data exchanges among the agents. A centralized mechanism determines the data sharing policies that optimize a network-wide objective function combining the fidelities and leakages at both agents. Using common-goal games and best-response analysis, the optimal policies are derived analytically and allow a distributed implementation. In contrast, in the decentralized setting, repeated discounted games are shown to naturally enable data exchange (without any central control or economic incentives) resulting from the power to renege on a mutual data exchange agreement. For both approaches, it is shown that non-zero data exchanges can be sustained for specific fidelity ranges even when privacy is a limiting factor. This paper makes a first contribution to understanding how data exchange among distributed agents can be enabled under privacy concerns and the resulting tradeoffs in terms of leakage vs. estimation errors.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2427775
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    ABSTRACT: We study the eavesdropping problem in the remotely distributed sensing of a privacy-sensible hypothesis from the Bayesian detection perspective. We consider a parallel distributed detection network where remote decision makers independently make local decisions defined on finite domains and forward them to the fusion center which makes the final decision. An eavesdropper is assumed to intercept a specific set of local decisions to make also a guess on the hypothesis. We propose a novel Bayesian detection-operational privacy metric given by the minimal achievable Bayesian risk of the eavesdropper. Further, we introduce two privacy-aware distributed Bayesian detection formulations, namely the privacy-constrained distributed Bayesian detection problem and the privacy-concerned distributed Bayesian detection problem where the detection performance is optimized under a privacy guarantee constraint and a weighted sum objective of the detection performance and privacy risk is minimized respectively. For an optimal privacy-aware distributed Bayesian detection design, the optimal decision strategy of employing a deterministic likelihood test or a randomized strategy thereof is identified. Further, it is shown that equivalent problems of different formulations always exist and lead to the same optimal privacy-aware distributed Bayesian detection design. The results are illustrated and discussed by numerical examples. The idea of privacy-aware distributed Bayesian detection design provides a novel solution to realize future trustworthy Internet of Things applications.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2429123
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    ABSTRACT: Collaborative filtering (CF) is the most popular recommendation algorithm, which exploits the collected historic user ratings to predict unknown ratings. However, traditional recommender systems run at the central servers, and thus users have to disclose their personal rating data to other parties. This raises the privacy issue, as user ratings can be used to reveal sensitive personal information. In this paper, we propose a semi-distributed belief propagation (BP) approach to privacy-preserving item-based CF recommender systems. Firstly, we formulate the item similarity computation as a probabilistic inference problem on the factor graph, which can be efficiently solved by applying the BP algorithm. To avoid disclosing user ratings to the server or other user peers, we then introduce a semi-distributed architecture for the BP algorithm. We further propose a cascaded BP scheme to address the practical issue that only a subset of users participate in BP during one time slot. We analyze the privacy of the semi-distributed BP from a information-theoretic perspective. We also propose a method that reduces the computational complexity at the user side. Through experiments on the MovieLens dataset, we show that the proposed algorithm achieves superior accuracy.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2426677
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    ABSTRACT: With the growing popularity of data mining, privacy has become an issue of growing importance. Privacy can be seen as a special type of goods, in a sense that it can be traded by the owner for incentives. In this paper, we consider a private data collecting scenario where a data collector buys data from multiple data owners and employs anonymization techniques to protect data owners' privacy. Anonymization causes a decline of data utility; therefore, the data owner can only sell his data at a lower price if his privacy is better protected. Can one pursue higher data utility while maintaining acceptable privacy? How to balance the trade-off between privacy protection and data utility is an important question for big data. Considering that different data owners treat privacy differently, and their privacy preferences are unknown to the collector, we propose a contract theoretic approach for data collector to deal with the trade-off. By designing an optimal contract, the collector can make rational decisions on how to pay the data owners, and more importantly, how he should protect the owners' privacy. We show that when the collector requires a large amount of data, he should ask data owners who care privacy less to provide as much as possible data. We also find that whenever the collector requires higher utility of data or the data becomes less profitable, the collector should provide a stronger protection of the owners' privacy. Performance of the proposed contract is evaluated by both numerical simulations and real data experiments.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2425798
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    ABSTRACT: Web search histories can reveal detailed and sensitive information about people. Private information retrieval (PIR) tackles this potential privacy violation by allowing users to retrieve the wth record of a database without revealing w to the server. However, most known PIR schemes are either very inefficient (and therefore unlikely to gain traction in a practical sense) or reliant on some restrictive assumptions. In this paper, we consider an efficient class of schemes called multi-server PIR. Multi-server PIR assumes that the client communicates with multiple, non-colluding servers, each possessing an identical copy of the database. Significant prior work has gone towards relaxing the anti-collusion assumption, but the literature does not address the assumption that servers store perfectly-synchronized databases. This seems implausible, especially if servers are not meant to collude. We propose the first multi-server PIR scheme to return the desired record even when servers’ databases are not perfectly synchronized. Our scheme asymptotically has the same computational and communication complexity as state-ofthe- art PIR schemes for synchronized databases; this comes at the expense of probabilistic success and two rounds of communication (most existing schemes require only one). Additionally, this approach efficiently processes multiple concurrent PIR queries.
    IEEE Journal of Selected Topics in Signal Processing 10/2015; 9(7):1-1. DOI:10.1109/JSTSP.2015.2432740
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    ABSTRACT: Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2414401
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    ABSTRACT: Airborne and spaceborne hyperspectral sensors, due to their limited spatial resolution, often record the spectral response of a mixture of materials. In order to extract the abundances of these materials, linear and nonlinear unmixing algorithms have been developed. In this paper, we focus on nonlinear mixing models that are able to model macro- and microscopic scale interactions. Although very useful, these models may be inverted only by means of optimization techniques, typically impossible to be performed in matrix form. Thereby, only nonlinear mixing models that describe macroscopic effects (e.g., two-reflections schemes) are currently considered as they have lower computational costs. On the other hand, this limitation may result in a loss in terms of description accuracy for the images. In this paper, we propose a new approach for nonlinear unmixing that aims at providing excellent reconstruction performance for arbitrary polynomial nonlinearities making use of the polytope decomposition (POD) method. Additionally, POD transforms nonlinear unmixing into a linear problem, and can be easily implemented in high-performance computing architectures. Results using synthetic and real data confirm the effectiveness and accuracy of the proposed framework. To prove its feasibility for fast computational applications, its complexity is analytically derived and compared with real data analysis.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2416693