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Establishing the Price of Privacy in Federated Data Trading
Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Personal data is becoming one of the most essential resources in today's information-based society. Accordingly, there is a growing interest in data markets, which operate data tra…
On the impossibility of non-trivial accuracy under fairness constraints
Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida +1
One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. prop…
An Incentive Mechanism for Trading Personal Data in Data Markets
Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi
With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this tre…
DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla +2
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations…
Bounding Information Leakage in Machine Learning
Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi +1
Recently, it has been shown that Machine Learning models can leak sensitive information about their training data. This information leakage is exposed through membership and attrib…