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Python library for direct and indirect discrimination prevention in data mining
This python library implements the discrimination discovery and prevention method proposed in the paper: “A methodology for direct and indirect discrimination prevention in...-
GitHub
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GitHub
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Tutorials on anonymization and antidiscrimination of datasets
These tutorials show how to use the already implemented libraries for the anonymization and antidiscrimination of data sets: - Anonymization library:...-
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ipynb
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ipynb
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Machine Learning Explainability Via Microaggregation and Shallow Decision Trees
Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian... -
Explanation of Deep Models with Limited Interaction for Trade Secret and Priv...
An ever-increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions... -
Human Mobility Data Privacy Risk Estimator
This method is a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual... -
Mobility data sharing: application potential and ethical issues webinar
4th SoBigData++ Awareness Panel Webinar Programme Decentralized anonymization of mobility data Speaker: Josep Domingo-Ferrer (Universitat Rovira i Virgili, Catalonia)... -
Machine Learning Explainability Through Comprehensible Decision Trees
The role of decisions made by machine learning algorithms in our lives is ever increasing. In reaction to this phenomenon, the European General Data Protection Regulation... -
Scikit-mobility
Scikit-mobility is a library for human mobility analysis in Python. The library allows to: represent trajectories and mobility flows with proper data structures, TrajDataFrame...-
URL
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URL
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Label flipping attacks in Federated Learning
The following experiments showcase Federated Learning using Scikit-learn.-
ipynb
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ipynb
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Membership Inference Attacks on ML Models
This collection of Jupyter notebooks implements membership inference attacks found in Salem et al. "ML-Leaks: Model and Data Independent Membership Inference Attacks and... -
Accountability for the Use of Algorithms in a Big Data Environment
Accountability is the ability to provide good reasons in order to explain and to justify actions, decisions, and policies for a (hypothetical) forum of persons or... -
Legal Materials as Big Data: (algo)Rithms Support Legal Interpretation. A Dia...
This webinar, which took place on 6 July 2021, focused on the interplay between legal data and data science. The webinar, entitled ‘Legal Materials as Big Data: (algo)Rithms to...-
.webloc
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.webloc
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General confidentiality and utility metrics for privacy-preserving data publi...
Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to... -
Big other Surveillance capitalism and the prospects of an information civiliz...
This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The... -
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... -
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... -
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....-
PDF
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PDF
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Big Data and Due Process Toward a Framework to Redress Predictive Privacy Harms
The rise of “Big Data” analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create...
