Presentations

1. Introduction to Privacy-preserving Techniques for AI at Online Summer Talk, University of Science, Ho Chi Minh City, Vietnam (05/2023)

   This presentation introduces the overview of state-of-the-art privacy-preserving techniques for AI applications: k-Anonymity, Differential Privacy, Homomorphic Encryption, and Federated Learning.

   [Slides]
2. Private Federated Learning Knowledge Graph Representation Learning at Lab Meeting, University of Insubria, Varese, Italy (10/2022)

   This presentation introduces my investigation on the privacy protection techniques for learning knowledge graph representation under federated learning settings by using differential privacy.

   [Slides]
3. Time-Aware Anonymization of Knowledge Graphs at the Cyber Security Competence for Research and Innovation Project (CONCORDIA) Meeting, Munich, Germany (06/2022)

   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.

   [Slides]
4. Transparency in Proximity Advertising Campaigns at the Cyber Security Competence for Research and Innovation Project (CONCORDIA) Meeting, Munich, Germany (06/2022)

   This presentation introduces my blockchain-based platform ensuring the transparency of marketing campaign effectiveness measurement and privacy of participants (marketers, publishers, and customers).

   [Slides]
5. Differential Privacy: Foundation, Applications, and Challenges at Lab Meeting, University of Insubria, Varese, Italy (09/2021)

   This presentation covers basic concepts of differential privacy.

   [Slides]
6. Privacy-Preserving Sequential Publishing of Knowledge Graphs at the 37th IEEE International Conference on Data Engineering, Chania, Greece (Virtual) (04/2021)

   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.

   [Slides]
7. Adversarial Machine Learning at Lab Meeting, University of Insubria, Varese, Italy (11/2020)

   This presentation introduces some adversarial machine learning attacks.

   [Slides]
8. Cluster-based Anonymization of Knowledge Graphs at the 18th International Conference on Applied Cryptography and Network Security, Rome, Italy (Virtual) (10/2020)

   This presentation introduces the first anonymization approach for knowledge graphs.

   [Slides]