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  • JournalArticle

    Privacy in the clouds

    Informational self-determination refers to the right or ability of individuals to exercise personal control over the collection, use and disclosure of their personal data by...
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  • JournalArticle

    Measuring discrimination in algorithmic decision making

    Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational...
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  • JournalArticle

    Explaining misclassification and attacks in deep learning via random forests

    Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...
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  • BookChapter

    Information Technology Privacy and the Protection of Personal Data

    Information technology allows us to generate, store, and process huge quantities of data. Search engines, satellites, sensor networks, scientists, security agencies,...
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  • JournalArticle

    Identifying and exploiting homogeneous communities in labeled networks

    Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a...
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  • JournalArticle

    Ethical Value Centric Cybersecurity. A Methodology Based on a Value Graph

    Our society is being shaped in a non-negligible way by the technological advances of recent years, especially in information and communications technologies (ICTs). The...
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  • TrainingMaterial

    Epidemics and city. How mobility and well being changed with COVID19 era

    How did the COVID-19 epidemics change our mobility habits, and how did it impact on people’s well-being and on the virus transmissibility? In the first webinar of the seminar...
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  • ConferencePaper

    Efficient detection of Byzantine attacks in federated learning using last lay...

    Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...
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  • ConferencePaper

    Fairness and Abstraction in Sociotechnical Systems

    A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as...
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  • ConferencePaper

    Fair detection of poisoning attacks in federated learning

    Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....
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  • JournalArticle

    Conformity a Path-Aware Homophily measure for Node-Attributed Networks

    Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...
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  • JournalArticle

    Designing for human rights in AI

    In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance...
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  • JournalArticle

    Democratizing Algorithmic Fairness

    Machine learning algorithms can now identify patterns and correlations in (big) datasets and predict outcomes based on the identified patterns and correlations. They can then...
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  • JournalArticle

    Big Data Ethics

    The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and...
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