Security Loophole in Error Verification in Quantum Key Distribution

The security of quantum key distribution QKD is evaluated based on the secrecy of Alice's key and the correctness of the keys held by Alice and Bob. A practical method for ensuring correctness is known as error verification, in which Alice and Bob reveal a portion of their reconciled keys and che...

Differentially Private Relational Learning with Entity-Level Privacy Guarantees

Learning with relational and network-structured data is increasingly vital in sensitive domains where protecting the privacy of individual entities is paramount. Differential Privacy DP offers a principled approach for quantifying privacy risks, with DP-SGD emerging as a standard mechanism for...

Certified Unlearning for Neural Networks

We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regulatory requirements such as the "right to be forgotten." Unfortunately, existing methods rely on restrictive assumptio...

Privacy Amplification through Synthetic Data: Insights from Linear Regression

Synthetic data inherits the differential privacy guarantees of the model used to generate it. Additionally, synthetic data may benefit from privacy amplification when the generative model is kept hidden. While empirical studies suggest this phenomenon, a rigorous theoretical understanding is stil...

Randextract: a Reference Library to Test and Validate Privacy Amplification Implementations

Quantum cryptographic protocols do not rely only on quantum-physical resources, they also require reliable classical communication and computation. In particular, the secrecy of any quantum key distribution protocol critically depends on the correct execution of the privacy amplification step. Th...