Internet Advertising Giants: Google & Facebook

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Related lectures (32)

Page 3 of 4

Privacy Engineering: Mechanisms and Principles

Covers privacy mechanisms, their pros and cons, and their application in various scenarios, emphasizing privacy as a security property and its significance in society.

Désanonymisation: Risks and Techniques

Explores risks and techniques in de-anonymizing data, including flaws in methods and real-world examples of failed attempts.

Privacy-Preserving Federated Analytics for Personalized Medicine

Delves into privacy-preserving federated analytics for personalized medicine, discussing challenges, solutions, and real-world applications.

Social Media: User-Generated Content and Business Models

Delves into social media, user-generated content, business models, targeted advertising, and misinformation challenges.

Model Compression Techniques: Enhancing Neural Networks

Covers model compression techniques to enhance the efficiency of large language models in production settings.

Deanonymizing Databases: IMDB vs Netflix

Explores deanonymizing IMDB and Netflix databases, revealing user identities and analyzing movie evaluations.

Machine Learning and Privacy

Delves into Differential Privacy trade-offs, disparate impact, and machine learning-based privacy attacks.

Influence: Social Interaction and Online Metrics

Discusses social influence, online metrics, Page Rank, and the impact of money.

Machine Learning and Privacy

Explores Federated Machine Learning and Differential Privacy in Machine Learning, discussing attacks, defenses, and challenges.

Genomic Data Privacy: Challenges and Solutions

Explores the evolution of personal genomics, from consumer testing to data sharing.