This presentation introduces my investigation on the privacy protection techniques for learning knowledge graph representation under federated learning settings by using differential privacy.
This presentation introduces my k-anonymity technique protecting data owners from identity and attribute leakage even though adversaries have access to all published anonymized versions of a knowledge graph.
This presentation introduces my blockchain-based platform ensuring the transparency of marketing campaign effectiveness measurement and privacy of participants (marketers, publishers, and customers).
This presentation covers basic concepts of differential privacy.
This presentation introduces the sequential anonymization approach for protecting users from identity leakage when adversaries have access to w continuous anonymized versions of a knowledge graph.
This presentation introduces some adversarial machine learning attacks.
This presentation introduces the first anonymization approach for knowledge graphs.