731 research outputs found

    Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

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    Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

    Contribuciones a la Seguridad del Aprendizaje Automático

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, leída el 05-11-2020Machine learning (ML) applications have experienced an unprecedented growth over the last two decades. However, the ever increasing adoption of ML methodologies has revealed important security issues. Among these, vulnerabilities to adversarial examples, data instances targeted at fooling ML algorithms, are especially important. Examples abound. For instance, it is relatively easy to fool a spam detector simply misspelling spam words. Obfuscation of malware code can make it seem legitimate. Simply adding stickers to a stop sign could make an autonomous vehicle classify it as a merge sign. Consequences could be catastrophic. Indeed, ML is designed to work in stationary and benign environments. However, in certain scenarios, the presence of adversaries that actively manipulate input datato fool ML systems to attain benefits break such stationarity requirements. Training and operation conditions are not identical anymore. This creates a whole new class of security vulnerabilities that ML systems may face and a new desirable property: adversarial robustness. If we are to trust operations based on ML outputs, it becomes essential that learning systems are robust to such adversarial manipulations...Las aplicaciones del aprendizaje automático o machine learning (ML) han experimentado un crecimiento sin precedentes en las últimas dos décadas. Sin embargo, la adopción cada vez mayor de metodologías de ML ha revelado importantes problemas de seguridad. Entre estos, destacan las vulnerabilidades a ejemplos adversarios, es decir; instancias de datos destinadas a engañar a los algoritmos de ML. Los ejemplos abundan: es relativamente fácil engañar a un detector de spam simplemente escribiendo mal algunas palabras características de los correos basura. La ofuscación de código malicioso (malware) puede hacer que parezca legítimo. Agregando unos parches a una señal de stop, se podría provocar que un vehículo autónomo la reconociese como una señal de dirección obligatoria. Cómo puede imaginar el lector, las consecuencias de estas vulnerabilidades pueden llegar a ser catastróficas. Y es que el machine learning está diseñado para trabajar en entornos estacionarios y benignos. Sin embargo, en ciertos escenarios, la presencia de adversarios que manipulan activamente los datos de entrada para engañar a los sistemas de ML(logrando así beneficios), rompen tales requisitos de estacionariedad. Las condiciones de entrenamiento y operación de los algoritmos ya no son idénticas, quebrándose una de las hipótesis fundamentales del ML. Esto crea una clase completamente nueva de vulnerabilidades que los sistemas basados en el aprendizaje automático deben enfrentar y una nueva propiedad deseable: la robustez adversaria. Si debemos confiaren las operaciones basadas en resultados del ML, es esencial que los sistemas de aprendizaje sean robustos a tales manipulaciones adversarias...Fac. de Ciencias MatemáticasTRUEunpu

    Rational Multiparty Computation

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    The field of rational cryptography considers the design of cryptographic protocols in the presence of rational agents seeking to maximize local utility functions. This departs from the standard secure multiparty computation setting, where players are assumed to be either honest or malicious. ^ We detail the construction of both a two-party and a multiparty game theoretic framework for constructing rational cryptographic protocols. Our framework specifies the utility function assumptions necessary to realize the privacy, correctness, and fairness guarantees for protocols. We demonstrate that our framework correctly models cryptographic protocols, such as rational secret sharing, where existing work considers equilibrium concepts that yield unreasonable equilibria. Similarly, we demonstrate that cryptography may be applied to the game theoretic domain, constructing an auction market not realizable in the original formulation. Additionally, we demonstrate that modeling players as rational agents allows us to design a protocol that destabilizes coalitions. Thus, we establish a mutual benefit from combining the two fields, while demonstrating the applicability of our framework to real-world market environments.^ We also give an application of game theory to adversarial interactions where cryptography is not necessary. Specifically, we consider adversarial machine learning, where the adversary is rational and reacts to the presence of a data miner. We give a general extension to classification algorithms that returns greater expected utility for the data miner than existing classification methods
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