Information-theoretic security under computational, bandwidth, and randomization constraints

The objective of the proposed research is to develop and analyze coding schemes for information-theoretic security, which could bridge a gap between theory an practice. We focus on two fundamental models for information-theoretic security: secret-key generation for a source model and secure communication over the wire-tap channel. Many results for these models only provide existence of codes, and few attempts have been made to design practical schemes. The schemes we would like to propose should account for practical constraints. Specifically, we formulate the following constraints to avoid oversimplifying the problems. We should assume: (1) computationally bounded legitimate users and not solely rely on proofs showing existence of code with exponential complexity in the block-length; (2) a rate-limited public communication channel for the secret-key generation model, to account for bandwidth constraints; (3) a non-uniform and rate-limited source of randomness at the encoder for the wire-tap channel model, since a perfectly uniform and rate-unlimited source of randomness might be an expensive resource. Our work focuses on developing schemes for secret-key generation and the wire-tap channel that satisfy subsets of the aforementioned constraints.Ph.D

Security for correlated sources across wiretap network

A thesis submitted in ful llment of the requirements for the degree of Doctor of Philosophy in the School of Electrical and Information Engineering Faculty of Engineering University of the Witwatersrand July 2015This thesis presents research conducted for the security aspects of correlated sources across a wiretap network. Correlated sources are present in communication systems where protocols ensure that there is some predetermined information for sources to transmit. Systems that contain correlated sources are for example broadcast channels, smart grid systems, wireless sensor networks and social media networks. In these systems there exist common information between the nodes in a network, which gives rise to security risks as common information can be determined about more than one source. In this work the security aspects of correlated sources are investigated. Correlated source coding in terms of the Slepian-Wolf theorem is investigated to determine the amount of information leakage for various correlated source models. The perfect secrecy approach developed by Shannon has also been incorporated as a security approach. In order to explore these security aspects the techniques employed range from typical sequences used to prove Slepian-Wolf's theorem to coding methods incorporating matrix partitions for correlated sources. A generalized correlated source model is presented and the procedure to determine the information leakage is initially illustrated using this model. A novel scenario for two correlated sources across a channel with eavesdroppers is also investigated. It is a basic model catering for the correlated source applications that have been detailed. The information leakage quanti cation is provided, where bounds specify the quantity of information leaked for various cases of eavesdropped channel information. The required transmission rates for perfect secrecy when some channel information has been wiretapped is further determined, followed by a method to reduce the key length required for perfect secrecy. The implementation thereafter provided shows how the information leakage is determined practically. In the same way using the information leakage quanti cation, Shannon's cipher system approach and practical implementation a novel two correlated source model where channel information and some source data symbols (predetermined information) are wiretapped is investigated. The adversary in this situation has access to more information than if a link is wiretapped only and can thus determine more about a particular source. This scenario caters for an application where the eavesdropper has access to some predetermined information. The security aspects and coding implementation have further been developed for a novel correlated source model with a heterogeneous encoding method. The model caters for situations where a wiretapper is able to easily access a particular source. iii The interesting link between information theory and coding theory is explored for the novel models presented in this research. A matrix partition method is utilized and the information leakage for various cases of wiretapped syndromes are presented. The research explores the security for correlated sources in the presence of wiretappers. Both the information leakage and Shannon's cipher system approach are used to achieve these security aspects. The implementation shows the practicality of using these security aspects in communications systems. The research contained herein is signi cant as evident from the various applications it may be used for and to the author's knowledge is novel

Polar codes for distributed source coding

Ankara : The Department of Electrical and Electronics Engineering and The Graduate School of Engineering and Science of Bilkent Univesity, 2014.Thesis (Ph. D.) -- Bilkent University, 2014.Includes bibliographical references leaves 164-170.Polar codes were invented by Arıkan as the first “capacity achieving” codes for binary-input discrete memoryless symmetric channels with low encoding and decoding complexity. The “polarization phenomenon”, which is the underlying principle of polar codes, can be applied to different source and channel coding problems both in single-user and multi-user settings. In this work, polar coding methods for multi-user distributed source coding problems are investigated. First, a restricted version of lossless distributed source coding problem, which is also referred to as the Slepian-Wolf problem, is considered. The restriction is on the distribution of correlated sources. It is shown that if the sources are “binary symmetric” then single-user polar codes can be used to achieve full capacity region without time sharing. Then, a method for two-user polar coding is considered which is used to solve the Slepian-Wolf problem with arbitrary source distributions. This method is also extended to cover multiple-access channel problem which is the dual of Slepian-Wolf problem. Next, two lossy source coding problems in distributed settings are investigated. The first problem is the distributed lossy source coding which is the lossy version of the Slepian-Wolf problem. Although the capacity region of this problem is not known in general, there is a good inner bound called the Berger-Tung inner bound. A polar coding method that can achieve the whole dominant face of the Berger-Tung region is devised. The second problem considered is the multiple description coding problem. The capacity region for this problem is also not known in general. El Gamal-Cover inner bound is the best known bound for this problem. A polar coding method that can achieve any point on the dominant face of El Gamal-Cover region is devised.Önay, SaygunPh.D

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

Compression pour la communication interactive de contenus visuels

Interactive images and videos have received increasing attention due to the interesting features they provide. With these contents, users can navigate within the content and explore the scene from the viewpoint they desire. The characteristics of these media make their compression very challenging. On the one hand, the data is captured in high resolution (very large) to experience a real sense of immersion. On the other hand, the user requests a small portion of the content during navigation. This requires two characteristics: efficient compression of data by exploiting redundancies within the content (to lower the storage cost), and random access ability to extract part of the compressed stream requested by the user (to lower the transmission rate). Classical compression schemes can not handle random accessibility because they use a fixed pre-defined order of sources to capture redundancies.The purpose of this thesis is to provide new tools for interactive compression schemes of images. For that, as the first contribution, we propose an evaluation framework by which we can compare different image/video interactive compression schemes. Moreover, former theoretical studies show that random accessibility can be achieved using incremental codes with the same transmission cost as non-interactive schemes and with reasonable storage overhead. Our second contribution is to build a generic coding scheme that can deal with various interactive media. Using this generic coder, we then propose compression tools for 360-degree images and 3D model texture maps with random access ability to extract the requested part. We also propose new representations for these modalities. Finally, we study the effect of model selection on the compression rates of these interactive coders.Les images et vidéos interactives ont récemment vu croître leur popularité. En effet, avec ce type de contenu, les utilisateurs peuvent naviguer dans la scène et changer librement de point de vue. Les caractéristiques de ces supports posent de nouveaux défis pour la compression. D'une part, les données sont capturées en très haute résolution pour obtenir un réel sentiment d'immersion. D'autre part, seule une petite partie du contenu est visualisée par l'utilisateur lors de sa navigation. Cela induit deux caractéristiques : une compression efficace des données en exploitant les redondances au sein du contenu (pour réduire les coûts de stockage) et une compression avec accès aléatoire pour extraire la partie du flux compressé demandée par l'utilisateur (pour réduire le débit de transmission). Les schémas classiques de compression ne peuvent gérer de manière optimale l’accès aléatoire, car ils utilisent un ordre de traitement des données fixe et prédéfini qui ne peut s'adapter à la navigation de l'utilisateur.Le but de cette thèse est de fournir de nouveaux outils pour les schémas interactifs de compression d’images. Pour cela, comme première contribution, nous proposons un cadre d’évaluation permettant de comparer différents schémas interactifs de compression d'image / vidéo. En outre, des études théoriques antérieures ont montré que l’accès aléatoire peut être obtenu à l’aide de codes incrémentaux présentant le même coût de transmission que les schémas non interactifs au prix d'une faible augmentation du coût de stockage. Notre deuxième contribution consiste à créer un schéma de codage générique pouvant s'appliquer à divers supports interactifs. À l'aide de ce codeur générique, nous proposons ensuite des outils de compression pour deux modalités d'images interactives : les images omnidirectionnelles (360 degrés) et les cartes de texture de modèle 3D. Nous proposons également de nouvelles représentations de ces modalités. Enfin, nous étudions l’effet de la sélection du modèle sur les taux de compression de ces codeurs interactifs

Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication

A new theory of mammalian hearing is presented, which accounts for the auditory image in the midbrain (inferior colliculus) of objects in the acoustical environment of the listener. It is shown that the ear is a temporal imaging system that comprises three transformations of the envelope functions: cochlear group-delay dispersion, cochlear time lensing, and neural group-delay dispersion. These elements are analogous to the optical transformations in vision of diffraction between the object and the eye, spatial lensing by the lens, and second diffraction between the lens and the retina. Unlike the eye, it is established that the human auditory system is naturally defocused, so that coherent stimuli do not react to the defocus, whereas completely incoherent stimuli are impacted by it and may be blurred by design. It is argued that the auditory system can use this differential focusing to enhance or degrade the images of real-world acoustical objects that are partially coherent. The theory is founded on coherence and temporal imaging theories that were adopted from optics. In addition to the imaging transformations, the corresponding inverse-domain modulation transfer functions are derived and interpreted with consideration to the nonuniform neural sampling operation of the auditory nerve. These ideas are used to rigorously initiate the concepts of sharpness and blur in auditory imaging, auditory aberrations, and auditory depth of field. In parallel, ideas from communication theory are used to show that the organ of Corti functions as a multichannel phase-locked loop (PLL) that constitutes the point of entry for auditory phase locking and hence conserves the signal coherence. It provides an anchor for a dual coherent and noncoherent auditory detection in the auditory brain that culminates in auditory accommodation. Implications on hearing impairments are discussed as well.Comment: 603 pages, 131 figures, 13 tables, 1570 reference

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms