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Alberto Blanco-Justicia
Researcher at Rovira i Virgili University
Publications - 34
Citations - 347
Alberto Blanco-Justicia is an academic researcher from Rovira i Virgili University. The author has contributed to research in topics: Anonymity & Computer science. The author has an hindex of 6, co-authored 28 publications receiving 94 citations.
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The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
TL;DR: Differential privacy is not a silver bullet for all privacy problems, but it can be a step forward in the right direction.
Journal ArticleDOI
The limits of differential privacy (and its misuse in data release and machine learning)
TL;DR: Differential privacy is a neat privacy definition that can coexist with certain well-defined data uses in the context of interactive queries as discussed by the authors, however, it is neither a silver bullet for all privacy problems nor a replacement for all previous privacy models.
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Machine learning explainability via microaggregation and shallow decision trees
TL;DR: This work focuses on explaining black-box models by using decision trees of limited depth as a surrogate model and proposes an approach based on microaggregation to achieve a trade-off between the comprehensibility and the representativeness of the surrogate model on the one side and the privacy of the subjects used for training the black-boxes on the other side.
Posted Content
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
Alberto Blanco-Justicia,Josep Domingo-Ferrer,Sergio Martínez,David Sánchez,Adrian Flanagan,Kuan Eeik Tan +5 more
TL;DR: This paper examines security and privacy attacks to FL and critically survey solutions proposed in the literature to mitigate each attack and sketches ways to tackle this open problem and attain bothSecurity and privacy protection.
Journal ArticleDOI
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
Alberto Blanco-Justicia,Josep Domingo-Ferrer,Sergio Martínez,David Sánchez,Adrian Flanagan,Kuan Eeik Tan +5 more
TL;DR: In this paper, the authors examine security and privacy attacks to federated learning and critically survey solutions proposed in the literature to mitigate each attack and discuss the difficulty of simultaneously achieving security and private protection.