Distributed Inference and Learning with Byzantine Data

We are living in an increasingly networked world with sensing networks of varying shapes and sizes: the network often comprises of several tiny devices (or nodes) communicating with each other via different topologies. To make the problem even more complicated, the nodes in the network can be unreliable due to a variety of reasons: noise, faults and attacks, thus, providing corrupted data. Although the area of statistical inference has been an active area of research in the past, distributed learning and inference in a networked setup with potentially unreliable components has only gained attention recently. The emergence of big and dirty data era demands new distributed learning and inference solutions to tackle the problem of inference with corrupted data. Distributed inference networks (DINs) consist of a group of networked entities which acquire observations regarding a phenomenon of interest (POI), collaborate with other entities in the network by sharing their inference via different topologies to make a global inference. The central goal of this thesis is to analyze the effect of corrupted (or falsified) data on the inference performance of DINs and design robust strategies to ensure reliable overall performance for several practical network architectures. Specifically, the inference (or learning) process can be that of detection or estimation or classification, and the topology of the system can be parallel, hierarchical or fully decentralized (peer to peer). Note that, the corrupted data model may seem similar to the scenario where local decisions are transmitted over a Binary Symmetric Channel (BSC) with a certain cross over probability, however, there are fundamental differences. Over the last three decades, research community has extensively studied the impact of transmission channels or faults on the distributed detection system and related problems due to its importance in several applications. However, corrupted (Byzantine) data models considered in this thesis, are philosophically different from the BSC or the faulty sensor cases. Byzantines are intentional and intelligent, therefore, they can optimize over the data corruption parameters. Thus, in contrast to channel aware detection, both the FC and the Byzantines can optimize their utility by choosing their actions based on the knowledge of their opponent’s behavior. Study of these practically motivated scenarios in the presence of Byzantines is of utmost importance, and is missing from the channel aware detection and fault tolerant detection literature. This thesis advances the distributed inference literature by providing fundamental limits of distributed inference with Byzantine data and provides optimal counter-measures (using the insights provided by these fundamental limits) from a network designer’s perspective. Note that, the analysis of problems related to strategical interaction between Byzantines and network designed is very challenging (NP-hard is many cases). However, we show that by utilizing the properties of the network architecture, efficient solutions can be obtained. Specifically, we found that several problems related to the design of optimal counter-measures in the inference context are, in fact, special cases of these NP-hard problems which can be solved in polynomial time. First, we consider the problem of distributed Bayesian detection in the presence of data falsification (or Byzantine) attacks in the parallel topology. Byzantines considered in this thesis are those nodes that are compromised and reprogrammed by an adversary to transmit false information to a centralized fusion center (FC) to degrade detection performance. We show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable (or blind) of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance. Optimal attacking strategies in certain cases have the minimax property and, therefore, the knowledge of these strategies has practical significance and can be used to implement a robust detector at the FC. In several practical situations, parallel topology cannot be implemented due to limiting factors, such as, the FC being outside the communication range of the nodes and limited energy budget of the nodes. In such scenarios, a multi-hop network is employed, where nodes are organized hierarchically into multiple levels (tree networks). Next, we study the problem of distributed inference in tree topologies in the presence of Byzantines under several practical scenarios. We analytically characterize the effect of Byzantines on the inference performance of the system. We also look at the possible counter-measures from the FC’s perspective to protect the network from these Byzantines. These counter-measures are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. For scenarios where this is not possible, Byzantine tolerant schemes, which use game theory and error-correcting codes, are developed that tolerate the effect of Byzantines while maintaining a reasonably good inference performance in the network. Going a step further, we also consider scenarios where a centralized FC is not available. In such scenarios, a solution is to employ detection approaches which are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors. For such networks, we analytically characterize the negative effect of Byzantines on the steady-state and transient detection performance of conventional consensus-based detection schemes. To avoid performance deterioration, we propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. Next, we exploit the statistical distribution of the nodes’ data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes’ data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules. The above considerations highlight the negative effect of the corrupted data on the inference performance. However, it is possible for a system designer to utilize the corrupted data for network’s benefit. Finally, we consider the problem of detecting a high dimensional signal based on compressed measurements with secrecy guarantees. We consider a scenario where the network operates in the presence of an eavesdropper who wants to discover the state of the nature being monitored by the system. To keep the data secret from the eavesdropper, we propose to use cooperating trustworthy nodes that assist the FC by injecting corrupted data in the system to deceive the eavesdropper. We also design the system by determining the optimal values of parameters which maximize the detection performance at the FC while ensuring perfect secrecy at the eavesdropper

Distributed detection and estimation in wireless sensor networks: resource allocation, fusion rules, and network security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a centralized approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

Distributed Detection and Estimation in Wireless Sensor Networks: Resource Allocation, Fusion Rules, and Network Security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a $centralized$ approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

Distributed Detection and Estimation in Wireless Sensor Networks: Resource Allocation, Fusion Rules, and Network Security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a $centralized$ approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

Network Propaganda

"Is social media destroying democracy? Are Russian propaganda or ""Fake news"" entrepreneurs on Facebook undermining our sense of a shared reality? A conventional wisdom has emerged since the election of Donald Trump in 2016 that new technologies and their manipulation by foreign actors played a decisive role in his victory and are responsible for the sense of a ""post-truth"" moment in which disinformation and propaganda thrives. Network Propaganda challenges that received wisdom through the most comprehensive study yet published on media coverage of American presidential politics from the start of the election cycle in April 2015 to the one year anniversary of the Trump presidency. Analysing millions of news stories together with Twitter and Facebook shares, broadcast television and YouTube, the book provides a comprehensive overview of the architecture of contemporary American political communications. Through data analysis and detailed qualitative case studies of coverage of immigration, Clinton scandals, and the Trump Russia investigation, the book finds that the right-wing media ecosystem operates fundamentally differently than the rest of the media environment. The authors argue that longstanding institutional, political, and cultural patterns in American politics interacted with technological change since the 1970s to create a propaganda feedback loop in American conservative media. This dynamic has marginalized centre-right media and politicians, radicalized the right wing ecosystem, and rendered it susceptible to propaganda efforts, foreign and domestic. For readers outside the United States, the book offers a new perspective and methods for diagnosing the sources of, and potential solutions for, the perceived global crisis of democratic politics.

Network Propaganda

"Is social media destroying democracy? Are Russian propaganda or ""Fake news"" entrepreneurs on Facebook undermining our sense of a shared reality? A conventional wisdom has emerged since the election of Donald Trump in 2016 that new technologies and their manipulation by foreign actors played a decisive role in his victory and are responsible for the sense of a ""post-truth"" moment in which disinformation and propaganda thrives. Network Propaganda challenges that received wisdom through the most comprehensive study yet published on media coverage of American presidential politics from the start of the election cycle in April 2015 to the one year anniversary of the Trump presidency. Analysing millions of news stories together with Twitter and Facebook shares, broadcast television and YouTube, the book provides a comprehensive overview of the architecture of contemporary American political communications. Through data analysis and detailed qualitative case studies of coverage of immigration, Clinton scandals, and the Trump Russia investigation, the book finds that the right-wing media ecosystem operates fundamentally differently than the rest of the media environment. The authors argue that longstanding institutional, political, and cultural patterns in American politics interacted with technological change since the 1970s to create a propaganda feedback loop in American conservative media. This dynamic has marginalized centre-right media and politicians, radicalized the right wing ecosystem, and rendered it susceptible to propaganda efforts, foreign and domestic. For readers outside the United States, the book offers a new perspective and methods for diagnosing the sources of, and potential solutions for, the perceived global crisis of democratic politics.

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Autonomous Trade Unions in Algeria: An Expression of Nonviolent Acts of Citizenship

Algeriassa toimii kymmeniä autonomisia ammattiyhdistyksiä. Niiden muodostama monitahoinen poliittinen ryhmä on osa maan hajanaista oppositioliikettä. Autonomiset ammattiyhdistykset ovat sosiaalisia joukkoliikkeitä, joiden tarkoituksena on puolustaa työläisten oikeuksia ihmisoikeusjärjestöjä, kansalaisyhteiskunnan yhdistyksiä sekä poliittisia puolueita järjestöverkostojen kautta. Yksikään oppositioryhmä, olivatpa kyseessä autonomiset ammattiyhdistykset, poliittiset puolueet tai muut kansalaisyhteiskunnan toimijat, ei ole onnistunut muodostamaan uskottavaa, yhtenäistä vastavoimaa valtaapitäville. Algerian ammattiyhdistysliikkeen kartoittamisen ja analysoinnin avulla hahmotetaan liikkeen verkostojen muotoutumista. Samalla sen yhteiskunnallisen muutoksen panosta problematisoidaan normatiivisen demokratian rakentamispuheen kautta. Tässä väitöskirjassa keskitytään viimeisten 30 vuoden aikana toimineisiin näkyvimpiin hallituksen vastaisiin ammattiyhdistysliikkeisiin, jotka perustettiin asteittain vuoden 1989 perustuslaillisen uudistuksen yhteydessä, sekä yksittäisten ammattiyhdistysten muodostamiin konfederaatioihin. Väitöskirja tarkastelee, kuinka maan viranomaiset hallinnoivat rauhanomaista sosiaalista protestia ja hallituksen vastaisten ammattiyhdistysten luomaa haastetta. Lisäksi pohditaan, kuinka autonomisten ammattiyhdistysliikkeiden aktivistien vaatimista kansalaisoikeuksista keskustellaan väkivallattomina kansalaisuustekoina (nonviolent acts of citizenship). Työn empiirinen materiaali koostuu etnografisesta kenttätyöstä, osallistuvasta havainnoinnista ja media-analyysistä. Osittaisen tiedon filosofian (partial knowledge) avulla on mahdollista ymmärtää sosiaalisia ilmiöitä kuten ammattiyhdistystoimintaa ja poliittista aktivismia. Samalla kun ammattiyhdistysliikkeitä tarkastellaan sosiaalisina toimijoina yhteiskunnallisella tasolla, tutkimus lähestyy yksittäisiä aktivisteja kansalaisuustekojen teoreettisesta viitekehyksestä. Se mahdollistaa kansalaisoikeuksien muotoutumisen ymmärtämisen tekemisen kautta sen sijaan, että kansalaisoikeudet hahmotetaan institutionaalisena statuksena. Analysoin haastatteluja ja tutkimusmateriaalia diskurssianalyysilla keskittyen vastakkaisten diskurssien rakentumiseen. Erilaiset ”solmukohdissa” (nodal points) muodostuvat identiteetit ja hegemoniset diskurssit – myös katvealueille jääneet diskurssit ja hajaantuneet toimijuudet – ilmentävät ymmärrystä sekä representaatioita maailmasta. Etenkin työhön, työoloihin ja -sopimuksiin liittyvät sosiaaliset epäkohdat osallistavat eritaustaisia aktivisteja poliittiseen toimintaan ympäri maata rikkoen stereotypioita, joita liitetään uskontoon, poliittiseen ideologiaan ja sukupuoleen muslimienemmistöisissä maissa.There are dozens of autonomous trade unions in Algeria, forming a heterogeneous political body within the dispersed opposition in the country. Autonomous trade unions are social movements that aim to defend workers’ rights through multiple organizational networks that consist of human rights groups, civil society associations and political parties. None of the single oppositional groups, whether autonomous trade unions, oppositional political parties or civil society actors, have succeeded in the formation of a credible, cohesive and unified alternative force to the state authorities in order to challenge the power elite in the country. In mapping and analyzing the trade union movement, its networks and development in Algeria as well as problematizing its functioning and contribution to social change through normatively expressed democracy building, this thesis concentrates on the most conspicuous oppositional unions and their created confederations over the last 30 years, since their official establishment amid 1989 constitutional reform. Secondly, it contemplates how the state authorities manage peaceful societal protest and the challenge presented by these oppositional unions. Thirdly, it explores how the citizenship demanded by these autonomous union activists are negotiated through nonviolent acts of citizenship in the public space. The empirical material of this thesis comprises ethnographic fieldwork, participant observation, interviews and media analysis. Through partial knowledge, it is possible to understand social phenomena such as trade unionism and political activism. While autonomous trade unions are observed as social actors within the societal level, this study approaches individual activists through the theoretical framework of acts of citizenship. It enables us to understand citizenship as transformation through acts instead of perceiving citizenship as an institutional status. I analyze interviews and other existing research material, such as media articles and other available literature, through discourse analyses, concentrating on the historical and contemporary construction of antagonistic discourses. Various identities and hegemonic discourses are shaped within nodal points, forming understanding and representations of the world, as well as dislocated discourses within ruptures and the emergence of split subjects. Political engagement involves activists around the country, from cities and villages. They depict and stress their political participation via social grievances especially related to work, working conditions and contracts of employment. Autonomous trade unions bring together members from varied backgrounds, breaking certain stereotypes related to religion, political ideology and gender associated with Muslim-majority countries

Global Sceptical Publics: From non-religious print media to ‘digital atheism’

Global Sceptical Publics is the first major study of the significance of different media for the (re)production of non-religious publics and publicity. While much work has documented how religious subjectivities are shaped by media, until now the crucial role of diverse media for producing and participating in religion-sceptical publics and debates has remained under-researched. With some chapters focusing on locations hitherto barely considered by scholarship on non-religion, the book places in comparative perspective how atheists, secularists and humanists engage with media – as means of communication and forming non-religious publics, but also on occasion as something to be resisted. Its conceptually rich interdisciplinary chapters thereby contribute important new insights to the growing field of non-religion studies and to scholarship on media and materiality more generally