Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

AIs as Trusted Third Parties

This is a truly fascinating paper: "Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography." The basic idea is that AIs can act as trusted third parties: Abstract: We often interact with untrusted parties. Prioritization of privacy can limit t...

BlackMatter Ransomware

Summary Actions You Can Take Now to Protect Against BlackMatter Ransomware • Implement and enforce backup and restoration policies and procedures. • Usestrong, unique passwords. • Usemulti-factor authentication. • Implement network segmentation and traversal monitoring. Note: this advisory uses t...

Microsoft Shifts to 'Coordinated Vulnerability Disclosure' Policy

Microsoft is changing the way in which it handles vulnerability disclosures, now moving to a model it calls coordinated vulnerability disclosure, in which the researcher and the vendor work together to verify a vulnerability and allow ample time for a patch. However, the new philosophy also...