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A case study of Switzerland’s iterative online-voting experiments, security failures, and scientist–government collaboration to rebuild electoral regulation.
Explores a privacy-preserving architecture for verifying health attestations, lab results, and vaccination history during a pandemic.
A Brazilian malware dataset reveals why internet banking threat models must account for country and culture.
Empirical evidence on memory-unsafe vulnerabilities and how developers respond to calls to move away from C and C++.
A demonstration of open-source software and inexpensive hardware for detecting fake 4G/LTE base stations, decoding broadcasts, mapping antennas, and identifying anomalies in real time.
Lessons from shark conservation for communicating about security and privacy without exploiting fear during crises.
Examines 115 software supply chain attacks and vulnerability disclosures to explain their impact and the security and policy changes needed to address them.
Explores how hardware features serve as security anchors yet introduce vulnerabilities, with examples of insecure configurations, architectural risks, and secure-hardware lifecycle approaches.
Explore how fault injection and cache side-channel attacks compromise deep neural networks and expose their architectures.
Why sandboxing alone cannot fully secure real-world software—and how memory safety can close gaps left by operating system limitations.
Learn how BeyondProd applies zero-trust principles to secure cloud-native services through identity, code provenance, isolation, and verified deployment.
Explore how to use naturally occurring and generated motivation to move employees from security awareness to secure action.
How malware that changes behavior across hosts or time can confound analysis and detection, and what large-scale ethical monitoring reveals.
Learn how incorporating opportunity cost and hidden organizational, user, employee, and societal costs can improve information security decisions.
Learn how public-interest AI auditors can adapt security-testing practices and legal strategies to investigate algorithmic bias and other AI harms.
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