Learning End-to-End Codes for the BPSK-constrained Gaussian Wiretap Channel

Finite-length codes are learned for the Gaussian wiretap channel in an end-to-end manner assuming that the communication parties are equipped with deep neural networks (DNNs), and communicate through binary phase-shift keying (BPSK) modulation scheme. The goal is to find codes via DNNs which allow a pair of transmitter and receiver to communicate reliably and securely in the presence of an adversary aiming at decoding the secret messages. Following the information-theoretic secrecy principles, the security is evaluated in terms of mutual information utilizing a deep learning tool called MINE (mutual information neural estimation). System performance is evaluated for different DNN architectures, designed based on the existing secure coding schemes, at the transmitter. Numerical results demonstrate that the legitimate parties can indeed establish a secure transmission in this setting as the learned codes achieve points on almost the boundary of the equivocation region

Conditional Mutual Information Neural Estimator

Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rate based on aggregated samples from the channel. Motivated by the fact that, in many communication schemes, the achievable transmission rate is determined by a conditional mutual information term, this paper focuses on neural-based estimators for this information-theoretic quantity. Our results are based on variational bounds for the KL-divergence and, in contrast to some previous works, we provide a mathematically rigorous lower bound. However, additional challenges with respect to the unconditional mutual information emerge due to the presence of a conditional density function which we address here.Comment: To be presented at ICASSP 202

A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends

This paper examines the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state-of-the-art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. We also introduce the family of various jamming attacks and their counter-measures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 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

Secure and Private Cloud Storage Systems with Random Linear Fountain Codes

An information theoretic approach to security and privacy called Secure And Private Information Retrieval (SAPIR) is introduced. SAPIR is applied to distributed data storage systems. In this approach, random combinations of all contents are stored across the network. Our coding approach is based on Random Linear Fountain (RLF) codes. To retrieve a content, a group of servers collaborate with each other to form a Reconstruction Group (RG). SAPIR achieves asymptotic perfect secrecy if at least one of the servers within an RG is not compromised. Further, a Private Information Retrieval (PIR) scheme based on random queries is proposed. The PIR approach ensures the users privately download their desired contents without the servers knowing about the requested contents indices. The proposed scheme is adaptive and can provide privacy against a significant number of colluding servers.Comment: 8 pages, 2 figure

Privacy and security in cyber-physical systems

Data privacy has attracted increasing attention in the past decade due to the emerging technologies that require our data to provide utility. Service providers (SPs) encourage users to share their personal data in return for a better user experience. However, users' raw data usually contains implicit sensitive information that can be inferred by a third party. This raises great concern about users' privacy. In this dissertation, we develop novel techniques to achieve a better privacy-utility trade-off (PUT) in various applications. We first consider smart meter (SM) privacy and employ physical resources to minimize the information leakage to the SP through SM readings. We measure privacy using information-theoretic metrics and find private data release policies (PDRPs) by formulating the problem as a Markov decision process (MDP). We also propose noise injection techniques for time-series data privacy. We characterize optimal PDRPs measuring privacy via mutual information (MI) and utility loss via added distortion. Reformulating the problem as an MDP, we solve it using deep reinforcement learning (DRL) for real location trace data. We also consider a scenario for hiding an underlying ``sensitive'' variable and revealing a ``useful'' variable for utility by periodically selecting from among sensors to share the measurements with an SP. We formulate this as an optimal stopping problem and solve using DRL. We then consider privacy-aware communication over a wiretap channel. We maximize the information delivered to the legitimate receiver, while minimizing the information leakage from the sensitive attribute to the eavesdropper. We propose using a variational-autoencoder (VAE) and validate our approach with colored and annotated MNIST dataset. Finally, we consider defenses against active adversaries in the context of security-critical applications. We propose an adversarial example (AE) generation method exploiting the data distribution. We perform adversarial training using the proposed AEs and evaluate the performance against real-world adversarial attacks.Open Acces

Enabling Technologies for 5G and Beyond: Bridging the Gap between Vision and Reality

It is common knowledge that the fifth generation (5G) of cellular networks will come with drastic transformation in the cellular systems capabilities and will redefine mobile services. 5G (and beyond) systems will be used for human interaction, in addition to person-to-machine and machine-to-machine communications, i.e., every-thing is connected to every-thing. These features will open a whole line of new business opportunities and contribute to the development of the society in many different ways, including developing and building smart cities, enhancing remote health care services, to name a few. However, such services come with an unprecedented growth of mobile traffic, which will lead to heavy challenges and requirements that have not been experienced before. Indeed, the new generations of cellular systems are required to support ultra-low latency services (less than one millisecond), and provide hundred times more data rate and connectivity, all compared to previous generations such as 4G. Moreover, they are expected to be highly secure due to the sensitivity of the transmitted information. Researchers from both academia and industry have been concerting significant efforts to develop new technologies that aim at enabling the new generation of cellular systems (5G and beyond) to realize their potential. Much emphasis has been put on finding new technologies that enhance the radio access network (RAN) capabilities as RAN is considered to be the bottleneck of cellular networks. Striking a balance between performance and cost has been at the center of the efforts that led to the newly developed technologies, which include non-orthogonal multiple access (NOMA), millimeter wave (mmWave) technology, self-organizing network (SON) and massive multiple-input multiple-output (MIMO). Moreover, physical layer security (PLS) has been praised for being a potential candidate for enforcing transmission security when combined with cryptography techniques. Although the main concepts of the aforementioned RAN key enabling technologies have been well defined, there are discrepancies between their intended (i.e., vision) performance and the achieved one. In fact, there is still much to do to bridge the gap between what has been promised by such technologies in terms of performance and what they might be able to achieve in real-life scenarios. This motivates us to identify the main reasons behind the aforementioned gaps and try to find ways to reduce such gaps. We first focus on NOMA where the main drawback of existing solutions is related to their poor performance in terms of spectral efficiency and connectivity. Another major drawback of existing NOMA solutions is that transmission rate per user decreases slightly with the number of users, which is a serious issue since future networks are expected to provide high connectivity. To this end, we develop NOMA solutions that could provide three times the achievable rate of existing solutions while maintaining a constant transmission rate per user regardless of the number of connected users. We then investigate the challenges facing mmWave transmissions. It has been demonstrated that such technology is highly sensitive to blockage, which limits its range of communication. To overcome this obstacle, we develop a beam-codebook based analog beam-steering scheme that achieves near maximum beamforming gain performance. The proposed technique has been tested and verified by real-life measurements performed at Bell Labs. Another line of research pursued in this thesis is investigating challenges pertaining to SON. It is known that radio access network self-planning is the most complex and sensitive task due to its impact on the cost of network deployment, etc., capital expenditure (CAPEX). To tackle this issue, we propose a comprehensive self-planning solution that provides all the planning parameters at once while guaranteeing that the system is optimally planned. The proposed scheme is compared to existing solutions and its superiority is demonstrated. We finally consider the communication secrecy problem and investigated the potential of employing PLS. Most of the existing PLS schemes are based on unrealistic assumptions, most notably is the assumption of having full knowledge about the whereabouts of the eavesdroppers. To solve this problem, we introduce a radically novel nonlinear precoding technique and a coding strategy that together allow to establish secure communication without any knowledge about the eavesdroppers. Moreover, we prove that it is possible to secure communications while achieving near transmitter-receiver channel capacity (the maximum theoretical rate)

An Overview of Physical Layer Security with Finite Alphabet Signaling

Providing secure communications over the physical layer with the objective of achieving secrecy without requiring a secret key has been receiving growing attention within the past decade. The vast majority of the existing studies in the area of physical layer security focus exclusively on the scenarios where the channel inputs are Gaussian distributed. However, in practice, the signals employed for transmission are drawn from discrete signal constellations such as phase shift keying and quadrature amplitude modulation. Hence, understanding the impact of the finite-alphabet input constraints and designing secure transmission schemes under this assumption is a mandatory step towards a practical implementation of physical layer security. With this motivation, this article reviews recent developments on physical layer security with finite-alphabet inputs. We explore transmit signal design algorithms for single-antenna as well as multi-antenna wiretap channels under different assumptions on the channel state information at the transmitter. Moreover, we present a review of the recent results on secure transmission with discrete signaling for various scenarios including multi-carrier transmission systems, broadcast channels with confidential messages, cognitive multiple access and relay networks. Throughout the article, we stress the important behavioral differences of discrete versus Gaussian inputs in the context of the physical layer security. We also present an overview of practical code construction over Gaussian and fading wiretap channels, and discuss some open problems and directions for future research