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02/24/2023
Defending Against Backdoor Attacks by Layer-wise Feature Analysis
Training deep neural networks (DNNs) usually requires massive training d...
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01/24/2023
Database Reconstruction Is Not So Easy and Is Different from Reidentification
In recent years, it has been claimed that releasing accurate statistical...
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11/03/2022
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
TREs are widely, and increasingly used to support statistical analysis o...
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07/13/2022
Enhanced Security and Privacy via Fragmented Federated Learning
In federated learning (FL), a set of participants share updates computed...
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07/08/2022
Bistochastic privacy
We introduce a new privacy model relying on bistochastic matrices, that ...
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07/05/2022
Defending against the Label-flipping Attack in Federated Learning
Federated learning (FL) provides autonomy and privacy by design to parti...
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07/02/2022
FL-Defender: Combating Targeted Attacks in Federated Learning
Federated learning (FL) enables learning a global machine learning model...
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06/09/2022
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
We review the use of differential privacy (DP) for privacy protection in...
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12/30/2021
Circuit-Free General-Purpose Multi-Party Computation via Co-Utile Unlinkable Outsourcing
Multiparty computation (MPC) consists in several parties engaging in joi...
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08/04/2021
Secure and Privacy-Preserving Federated Learning via Co-Utility
The decentralized nature of federated learning, that often leverages the...
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12/12/2020
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
Federated learning (FL) allows a server to learn a machine learning (ML)...
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11/04/2020
The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
Differential privacy (DP) is a neat privacy definition that can co-exist...
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10/21/2020
Multi-Dimensional Randomized Response
In our data world, a host of not necessarily trusted controllers gather ...
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10/07/2020
General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model
Anonymization for privacy-preserving data publishing, also known as stat...
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04/10/2020
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
The rapid dynamics of COVID-19 calls for quick and effective tracking of...
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12/21/2018
The future of statistical disclosure control
Statistical disclosure control (SDC) was not created in a single seminal...
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08/03/2018
How to Avoid Reidentification with Proper Anonymization
De Montjoye et al. claimed that most individuals can be reidentified fro...
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03/06/2018
Connecting Randomized Response, Post-Randomization, Differential Privacy and t-Closeness via Deniability and Permutation
We explore some novel connections between the main privacy models in use...
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02/27/2012
