Digital Voting Systems: Security and Verifiability
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Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Explores challenges in the IoT era, resource-constrained nodes, complex algorithms, security solutions, and blockchain adoption for secure data storage.
Explores the challenges of protecting location privacy and various techniques to mitigate location-related inferences, highlighting the importance of trust assumptions and practical issues.
Explores privacy-preserving data publishing mechanisms, including k-anonymity and differential privacy, and their practical applications and challenges.
Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.
