A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication

Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical-layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical-layer message authentication is also introduced briefly. The survey concludes with observations on potential research directions in this area.Comment: 23 pages, 10 figures, 303 refs. arXiv admin note: text overlap with arXiv:1303.1609 by other authors. IEEE Communications Surveys and Tutorials, 201

Effects of Correlation of Channel Gains on the Secrecy Capacity in the Gaussian Wiretap Channel

Secrecy capacity is one of the most important characteristic of a wireless communication channel. Therefore, the study of this characteristic wherein the system has correlated channel gains and study them for different line-of-sight (LOS) propagation scenarios is of ultimate importance. The primary objective of this thesis from the mathematical side is to determine the secrecy capacity (SC) for correlated channel gains for the main and eavesdropper channels in a Gaussian Wiretap channel as a function from main parameters (μ, Σ, ρ). f(h1, h2) is the joint distribution of the two channel gains at channel use (h1, h2), fi(hi) is the main distribution of the channel gain hi. The results are based on assumption of the Gaussian distribution of channel gains (gM, gE). The main task of estimating the secrecy capacity is reduced to the problem of solving linear partial differential equations (PDE). Different aspects of the analysis of secrecy capacity considered in this research are the Estimation of SC mathematically and numerically for correlated SISO systems and a mathematical example for MIMO systems with PDE. The variations in Secrecy Capacity are studied for Rayleigh (NLOS) distribution and Rician (LOS) distribution. Suitable scenarios are identified in which secure communication is possible with correlation of channel gains. Also, the new algorithm using PDE has a higher speed and than analog algorithms constructed on the classical statistical Monte Carlo methods. Taking into account the normality of the distribution of system parameters, namely the channel gain (gM, gE), the algorithm is constructed for systems of partial differential equations which satisfies the secrecy criterion. Advisor: H. Andrew Harm

Secret Channel Training to Enhance Physical Layer Security With a Full-Duplex Receiver

This work proposes a new channel training (CT) scheme for a full-duplex receiver to enhance physical layer security. Equipped with NB full-duplex antennas, the receiver simultaneously receives the information signal and transmits artificial noise (AN). In order to reduce the non-cancellable self-interference due to the transmitted AN, the receiver has to estimate the self-interference channel prior to the data communication phase. In the proposed CT scheme, the receiver transmits a limited number of pilot symbols which are known only to itself. Such a secret CT scheme prevents an eavesdropper from estimating the jamming channel from the receiver to the eavesdropper, hence effectively degrading the eavesdropping capability. We analytically examine the connection probability (i.e., the probability of the data being successfully decoded by the receiver) of the legitimate channel and the secrecy outage probability due to eavesdropping for the proposed secret CT scheme. Based on our analysis, the optimal power allocation between CT and data/AN transmission at the legitimate transmitter/receiver is determined. Our examination shows that the newly proposed secret CT scheme significantly outperforms the non-secret CT scheme that uses publicly known pilots when the number of antennas at the eavesdropper is larger than one.ARC Discovery Projects Grant DP15010390

Privacy Protection, At What Cost? Exploring the Regulatory Resistance to Data Technology in Auto Insurance

Regulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such “usage-based insurance” induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs

Corruption and Pro-Poor Growth Outcomes: Evidence and Lessons for African Countries

There is growing consensus that corruption hurts economic performance by reducing private investment, adversely affecting the quantity and quality of public infrastructure, reducing tax revenue, and reducing human capital accumulation. In addition to inefficiency effects—lower growth for a given endowment in factors and technology—corruption also has adverse distributional effects as it hurts the poor disproportionately. For a given level of government budget and national income, high corruption countries have lower literacy rates, higher mortality rates, and overall worse human development outcomes. Corruption deepens poverty by reducing pro-poor pubic expenditures, creating artificial shortages and congestion in public services, and inducing a policy bias in favor of capital intensity, which perpetuates unemployment. High levels of corruption in African countries constitute one of the factors behind slow growth and limited progress in poverty reduction. Eradicating corruption in African bureaucracies is a challenging task, especially because it is a systemic phenomenon with effects that often lag far behind the causes. Therefore, explicit strategies are necessary to change the incentive structure by modifying the payoffs and sanctions that govern the interactions between bureaucrats and private economic operators. Strategies to fight corruption include measures to increase transparency in the management of public resources, establishing an incentive structure that rewards honest behavior among civil servants, enforcing transparency in international contracts and equal penalties to all parties to corrupt deals, and promotion of a free and responsible media.Corruption; pro-poor growth; rent-seeking; African countries

I\u27m afraid [of] my future. : Secrecy, Biopower, and Korean High School Girls

This paper analyzes the secrets revealed by Korean high school girls. Despite their struggles being known to the Korean public-at-large, the majority of these secrets express the students dismay at not meeting the high standards expected of teenage girls in successfully preparing for the future. In this case study, the public airing of the otherwise silenced acknowledgement of the authors\u27 perceived deficiencies and failures illuminates processes of biopower (the subject-based regulation and disciplining of bodies) embedded within the Korean nation-state building project. I explore how the Neo-Confucian principles of reverence, obedience, and self-cultivation work together with the neoliberal, post-industrial consumerist nation-state in rendering adolescent girls \u27docile and useful\u27 (Foucault 1990[1976]). I aim to explicate the regulatory mechanisms and disciplinary pressures experienced daily by Korean teenage girls and argue that adolescent subjects in particular embody both the underpinnings and cost of the nation-state building project.\u2

Towards Practical and Secure Channel Impulse Response-based Physical Layer Key Generation

Der derzeitige Trend hin zu “smarten” Geräten bringt eine Vielzahl an Internet-fähigen und verbundenen Geräten mit sich. Die entsprechende Kommunikation dieser Geräte muss zwangsläufig durch geeignete Maßnahmen abgesichert werden, um die datenschutz- und sicherheitsrelevanten Anforderungen an die übertragenen Informationen zu erfüllen. Jedoch zeigt die Vielzahl an sicherheitskritischen Vorfällen im Kontext von “smarten” Geräten und des Internets der Dinge auf, dass diese Absicherung der Kommunikation derzeit nur unzureichend umgesetzt wird. Die Ursachen hierfür sind vielfältig: so werden essentielle Sicherheitsmaßnahmen im Designprozess mitunter nicht berücksichtigt oder auf Grund von Preisdruck nicht realisiert. Darüber hinaus erschwert die Beschaffenheit der eingesetzten Geräte die Anwendung klassischer Sicherheitsverfahren. So werden in diesem Kontext vorrangig stark auf Anwendungsfälle zugeschnittene Lösungen realisiert, die auf Grund der verwendeten Hardware meist nur eingeschränkte Rechen- und Energieressourcen zur Verfügung haben. An dieser Stelle können die Ansätze und Lösungen der Sicherheit auf physikalischer Schicht (physical layer security, PLS) eine Alternative zu klassischer Kryptografie bieten. Im Kontext der drahtlosen Kommunikation können hier die Eigenschaften des Übertragungskanals zwischen zwei legitimen Kommunikationspartnern genutzt werden, um Sicherheitsprimitive zu implementieren und damit Sicherheitsziele zu realisieren. Konkret können etwa reziproke Kanaleigenschaften verwendet werden, um einen Vertrauensanker in Form eines geteilten, symmetrischen Geheimnisses zu generieren. Dieses Verfahren wird Schlüsselgenerierung basierend auf Kanalreziprozität (channel reciprocity based key generation, CRKG) genannt. Auf Grund der weitreichenden Verfügbarkeit wird dieses Verfahren meist mit Hilfe der Kanaleigenschaft des Empfangsstärkenindikators (received signal strength indicator, RSSI) realisiert. Dies hat jedoch den Nachteil, dass alle physikalischen Kanaleigenschaften auf einen einzigen Wert heruntergebrochen werden und somit ein Großteil der verfügbaren Informationen vernachlässigt wird. Dem gegenüber steht die Verwendung der vollständigen Kanalzustandsinformationen (channel state information, CSI). Aktuelle technische Entwicklungen ermöglichen es zunehmend, diese Informationen auch in Alltagsgeräten zur Verfügung zu stellen und somit für PLS weiterzuverwenden. In dieser Arbeit analysieren wir Fragestellungen, die sich aus einem Wechsel hin zu CSI als verwendetes Schlüsselmaterial ergeben. Konkret untersuchen wir CSI in Form von Ultrabreitband-Kanalimpulsantworten (channel impulse response, CIR). Für die Untersuchungen haben wir initial umfangreiche Messungen vorgenommen und damit analysiert, in wie weit die grundlegenden Annahmen von PLS und CRKG erfüllt sind und die CIRs sich grundsätzlich für die Schlüsselgenerierung eignen. Hier zeigen wir, dass die CIRs der legitimen Kommunikationspartner eine höhere Ähnlichkeit als die eines Angreifers aufzeigen und das somit ein Vorteil gegenüber diesem auf der physikalischen Schicht besteht, der für die Schlüsselgenerierung ausgenutzt werden kann. Basierend auf den Ergebnissen der initialen Untersuchung stellen wir dann grundlegende Verfahren vor, die notwendig sind, um die Ähnlichkeit der legitimen Messungen zu verbessern und somit die Schlüsselgenerierung zu ermöglichen. Konkret werden Verfahren vorgestellt, die den zeitlichen Versatz zwischen reziproken Messungen entfernen und somit die Ähnlichkeit erhöhen, sowie Verfahren, die das in den Messungen zwangsläufig vorhandene Rauschen entfernen. Gleichzeitig untersuchen wir, inwieweit die getroffenen fundamentalen Sicherheitsannahmen aus Sicht eines Angreifers erfüllt sind. Zu diesem Zweck präsentieren, implementieren und analysieren wir verschiedene praktische Angriffsmethoden. Diese Verfahren umfassen etwa Ansätze, bei denen mit Hilfe von deterministischen Kanalmodellen oder durch ray tracing versucht wird, die legitimen CIRs vorherzusagen. Weiterhin untersuchen wir Machine Learning Ansätze, die darauf abzielen, die legitimen CIRs direkt aus den Beobachtungen eines Angreifers zu inferieren. Besonders mit Hilfe des letzten Verfahrens kann hier gezeigt werden, dass große Teile der CIRs deterministisch vorhersagbar sind. Daraus leitet sich der Schluss ab, dass CIRs nicht ohne adäquate Vorverarbeitung als Eingabe für Sicherheitsprimitive verwendet werden sollten. Basierend auf diesen Erkenntnissen entwerfen und implementieren wir abschließend Verfahren, die resistent gegen die vorgestellten Angriffe sind. Die erste Lösung baut auf der Erkenntnis auf, dass die Angriffe aufgrund von vorhersehbaren Teilen innerhalb der CIRs möglich sind. Daher schlagen wir einen klassischen Vorverarbeitungsansatz vor, der diese deterministisch vorhersagbaren Teile entfernt und somit das Eingabematerial absichert. Wir implementieren und analysieren diese Lösung und zeigen ihre Effektivität sowie ihre Resistenz gegen die vorgeschlagenen Angriffe. In einer zweiten Lösung nutzen wir die Fähigkeiten des maschinellen Lernens, indem wir sie ebenfalls in das Systemdesign einbringen. Aufbauend auf ihrer starken Leistung bei der Mustererkennung entwickeln, implementieren und analysieren wir eine Lösung, die lernt, die zufälligen Teile aus den rohen CIRs zu extrahieren, durch die die Kanalreziprozität definiert wird, und alle anderen, deterministischen Teile verwirft. Damit ist nicht nur das Schlüsselmaterial gesichert, sondern gleichzeitig auch der Abgleich des Schlüsselmaterials, da Differenzen zwischen den legitimen Beobachtungen durch die Merkmalsextraktion effizient entfernt werden. Alle vorgestellten Lösungen verzichten komplett auf den Austausch von Informationen zwischen den legitimen Kommunikationspartnern, wodurch der damit verbundene Informationsabfluss sowie Energieverbrauch inhärent vermieden wird

Reining in the Big Promise of Big Data: Transparency, Inequality, and New Regulatory Frontiers

The growing differentiation of services based on Big Data harbors the potential for both greater societal inequality and for greater equality. Anti-discrimination law and transparency alone, however, cannot do the job of curbing Big Data’s negative externalities while fostering its positive effects. To rein in Big Data’s potential, we adapt regulatory strategies from behavioral economics, contracts and criminal law theory. Four instruments stand out: First, active choice may be mandated between data collecting-services (paid by data) and data-free services (paid by money). Our suggestion provides concrete estimates for the price range of a data-free option, sheds new light on the monetization of data-collecting services, and proposes an “inverse predatory pricing” instrument to limit excessive pricing of the data-free option. Second, we propose using the doctrine of unconscionability to prevent contracts that unreasonably favor data-collecting companies. Third, we suggest democratizing data collection by regular user surveys and data compliance officers partially elected by users. Finally, we trace back new Big Data personalization techniques to the old Hartian precept of treating like cases alike and different cases – differently. If it is true that a speeding ticket over $50 is less of a disutility for a millionaire than for a welfare recipient, the income and wealth-responsive fines powered by Big Data that we suggest offer a glimpse into the future of the mitigation of economic and legal inequality by personalized law. Throughout these different strategies, we show how salience of data collection can be coupled with attempts to prevent discrimination and exploitation of users. Finally, we discuss all four proposals in the context of different test cases: social media, student education software and credit and cell phone markets. Many more examples could and should be discussed. In the face of increasing unease about the asymmetry of power between Big Data collectors and dispersed users, about differential legal treatment, and about the unprecedented dimensions of economic inequality, this paper proposes a new regulatory framework and research agenda to put the powerful engine of Big Data to the benefit of both the individual and societies adhering to basic notions of equality and non-discrimination