The characteristic inventiveness of the United States is critically dependent on leading technological institutions rising to the occasion to create the next generat ion of leaders who are entrepreneurial in their thinking. This is especially true given recent data showing the significant impact of independent entrepreneurs on the growth of free-enterprise economies.

In this paper, we propose a new Expectation-Propagation (EP) algorithm to address the problem of joint robust linear regression and sparse anomaly detection from data corrupted by Poisson noise. Adopting an approximate Bayesian approach, an EP method is derived to approximate the posterior distribution of interest. The method accounts not only for additive anomalies, but also for destructive anomalies, i.e., anomalies that can lead to observations with amplitudes lower than the expected signals. Experiments conducted with both synthetic and real data illustrate the potential benefits of the proposed EP method in joint spectral unmixing and anomaly detection in the photon-starved regime of a Lidar system.

This paper addresses the problem of robust estimation of range profiles from single-photon Lidar waveforms associated with single surfaces using a simple model. In contrast to existing methods explicitly modeling nuisance photon detection events, the observation model considered neglects such events and the depth parameters are instead estimated using a cost function which is robust to model mismatch. More precisely, the family of β-divergences is considered instead of the classical likelihood function. This reformulation allows the weights of the observations to be balanced depending on the amount of robustness required. The performance of our approach is assessed through a series of experiments using synthetic data under different observation scenarios. The obtained results demonstrate a significant improvement of the robustness of the estimation compared to state-of-the-art pixelwise methods, for different background illumination and imaging scenarios.

Social media like Twitter has been widely adopted for information dissemination due to its convenience and efficiency. However, false information and rumors on social media are undermining its utility as a valuable real-time information source. Existing works for information credibility analysis are based on offline batch analysis, often incurring a long lag since the event first occurs. In this paper, we develop a generative probabilistic model for real-time event credibility prediction in Twitter. We propose an online prediction algorithm based on streaming tweets, without storing or reprocessing the past tweets. We evaluate both the offline batch prediction and online streaming prediction performance of the proposed model on the Twitter dataset. The empirical results show that its batch prediction performance outperforms other algorithms based on aggregation analysis, and the online prediction performance quickly approaches that of the batch prediction with only a few hundred tweets.

This paper presents the results of a comprehensive study of the shingled writing process and various signal processing and data detection approaches applied to the readback waveforms. The recording simulations include realistic head fields, a random granular media, magnetostatic and exchange interactions, and a READ head sensitivity function. Readback waveforms are examined in both one and two dimensions in terms of signal characteristics (linear and nonlinear), noise behavior (stationary and signal-dependent), and intertrack interference. Different equalization and detection approaches are compared and about a 10% density gain is reported for such 2-D magnetic recording compared with traditional single-track recording. These gains depend strongly on the number of readers, the reader positioning, and the reader width.

Spectrum sensing in a cognitive radio network is an essential technique that makes secondary users to detect the presence of primary users. Furthermore, the secondary users can make use of sensing results to help the primary users' transmission in a way to forward them in reward for allowing the spectrum access. However, for the primary users, the spectrum sensing can be considered eavesdropping in the sense that the secondary users may try to decode primary users' messages based on the sensing results. In this paper, by applying the notion of information-theoretic secrecy to the cognitive radio scenario, we propose a secure cooperative transmission scheme targeting at allowing the secondary users to sense and relay but making them ignorant of the primary users' message. Wiretap channel coding is applied to an encoding process of primary users, and the secondary users help the primary users' transmission in a way to forward the amplified sensing results combined with their own messages. We characterize an achievable secrecy rate and data rate pair that primary and secondary users can achieve and formulate three optimization problems from which the secondary transmitter carefully distributes transmit power between the relaying signal and its own message. The numerical results show that our scheme has a non-zero positive secrecy rate in the area where non-cooperative scheme achieves a zero secrecy rate.

We analyze the role of jamming as a means to increase the security of wireless systems. Specifically, we characterize the impact of cooperative/friendly jamming on the secrecy outage probability of a quasi-static wiretap fading channel. We introduce jamming coverage and jamming efficiency as security metrics, and evaluate the performance of three different jamming strategies that rely on various levels of channel state information. The analysis provides insight for the design of optimal jamming configurations and indicates that one jammer is not enough to maximize both metrics simultaneously.

While secrecy in communication systems has historically been obtained through cryptographic means in the upper layers, recent research efforts have focused on the physical layer and have unveiled ample opportunities for security design. In particular, the combination of signal processing techniques with channel coding for secrecy has been central to the development of physical-layer security efforts. Although implicit coding techniques for secrecy have been known since the 1970s, explicit code constructions have only been discovered within the last decade. The purpose of this article is to provide a synopsis of the state of the art in coding for secrecy. We discuss the general principles of coding, and we illustrate them with several examples. In particular, we discuss the importance of a nested code structure and stochastic encoding, which allow for both data reliability and security.

In wireless sensor networks (WSNs), security and energy consumption have been considered as long-lasting technical challenges as sensors usually suffer from complexity and energy constraints. In this paper, we study a simple and efficient physical-layer security to provide data confidentiality in a distributed detection scenario. In particular, to prevent passive eavesdropping on transmitting data from sensors to an ally fusion center (AFC), we propose a novel encryption scheme and decision fusion rules for a parallel access channel model. The proposed scheme takes advantage of a free natural resource, i.e., randomness of wireless channels, to encrypt the binary local decision of each sensor in such a way that the binary local decision is flipped according to instantaneous channel gain between the sensor and AFC. The location-specific and reciprocal properties of wireless channels enable the sensor and AFC to share the inherent randomness of wireless channels which are not available to an eavesdropper. Furthermore, it is shown that the scheme is well-suited to a low complexity and energy efficient modulation technique, noncoherent binary frequency shift keying. To evaluate performances of the proposed scheme, log-likelihood-ratio-based decision fusion strategies at the AFC are analyzed, and comparisons of decision performances are carried out. In addition, we prove that the proposed scheme achieves perfect secrecy with a simple structure that is suited for sensors of limited complexity.

In this paper, we analyze an attack scenario for the simple substitution cipher using the wiretap channel model, where the attacker only has access to error-prone ciphertext at the output of a packet erasure channel (PEC). Each packet is comprised of exactly one symbol of ciphertext, and hence, the attacker's channel could be viewed as a symbol erasure channel. Information-theoretic cryptanalysis provides key and message equivocations for the cipher in general, and then gives the results as functions of the error-free ciphertext equivocations. The findings characterize the increase in equivocation that might be expected if encrypted data were further encoded using wiretap codes that introduce symbol erasures to passive eavesdroppers.

Recent accomplishments in physical-layer security research have shown that channel coding for secrecy can be effectively combined with security at other layers, such as cryptography at the application layer, in order to provide a significant security enhancement to communication systems. The goal of this previous work was to inhibit the passive eavesdropper in the wiretap channel model by encoding the ciphertext using nonsystematic low-density parity-check (LDPC) codes prior to transmission and by exploiting the advantage of feedback for legitimate parties. The net result was propagation of a single packet erasure to the detriment of the entire message. The security enhancement was characterized assuming statistically independent packet erasure channels (PECs) for the legitimate receiver and the eavesdropper. In this paper, we go beyond these results by addressing correlated erasure events across the two channels in a wiretap feedback framework. The intuitive notion that high correlation across channels reduces secrecy is shown through the complete characterization of the correlated channel scenario. Furthermore, it is shown that security improvements are still achievable in the face of positive correlation by means of judicious physical-layer design, even when the eavesdropper has a better channel than the legitimate receiver.

A multilayer security solution for digital communication systems is provided by considering the joint effects of physical-layer security channel codes with applicationlayer cryptography. We address two problems: first, the cryptanalysis of error-prone ciphertext; second, the design of a practical physical-layer security coding scheme.To our knowledge, the cryptographic attack model of the noisy-ciphertext attack is a novel concept. The more traditional assumption that the attacker has the ciphertext is generally assumed when performing cryptanalysis. However, with the ever-increasing amount of viable research in physical-layer security, it now becomes essential to perform the analysis when ciphertext is unreliable. We do so for the simple substitution cipher using an information-theoretic framework, and for stream ciphers by characterizing the success or failure of fast-correlation attacks when the ciphertext contains errors.We then present a practical coding scheme that can be used in conjunction with cryptography to ensure positive error rates in an eavesdropper's observed ciphertext, while guaranteeing error-free communications for legitimate receivers. Our codes are called stopping set codes, and provide a blanket of security that covers nearly all possible system configurations and channel parameters. The codes require a public authenticated feedback channel.The solutions to these two problems indicate the inherent strengthening of security that can be obtained by confusing an attacker about the ciphertext, and then give a practical method for providing the confusion. The aggregate result is a multilayer security solution for transmitting secret data that showcases security enhancements over standalone cryptography.

In this paper, we consider a cooperative transmission scheme to ensure data confidentiality from passive eavesdropping in a distributed detection scenario. A wireless sensor Network (WSN) consisting a set of sensors observes an unknown target and reports sensing data to an ally fusion center (AFC). Meanwhile, an enemy fusion center (EFC) located in a vicinity of the AFC tries to eavesdrop on the reporting data in wireless environment. To prevent such an attack, we propose reporting rules for selected sensors to minimize not only the total error probability at the AFC but also information leakage to the EFC. While a set of sensors transmits sensing data to the AFC, the proposed scheme also activates another set of sensors aiming at inducing interference and making the EFC confused. Our results show that information-theoretic perfect secrecy can be achieved by taking advantage of random behavior of wireless parallel access channels.