Lecture

Location Privacy

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Related lectures (64)

Machine Learning and Privacy

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

Anonymous communications: Quantifying Anonymity

Delves into quantifying anonymity, naive approaches, DC-networks, and resolving collisions.

Privacy-preserving data publishing: Synthetic Data Generation

Explores synthetic data generation for privacy-preserving data publishing, evaluating its effectiveness against privacy threats in raw datasets.

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 Data Publishing: RAPPOR and Differential Privacy

Explores RAPPOR, Differential Privacy, Apple's implementation, k-means clustering, and challenges in implementing differential privacy.

Trust and Privacy

Explores trust, privacy, visual protection, social media models, and blockchain technology.

Privacy Engineering: Design Principles and SwissCovid Case Study

Explores Privacy by Design principles, data minimization, trust minimization, and the SwissCovid app case study.

Privacy-preserving data publishing

Explores privacy risks in data publishing, failed de-identification attempts, and the use of synthetic data for privacy protection.

Anonymous Communication: Motivations and Approaches

Explores motivations for anonymous communication and various approaches to achieve anonymity.

Machine Learning Security

Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.

Anonymous communications

Explores the significance of anonymous communications in safeguarding privacy and security, covering challenges, applications, system design, and quantifying anonymity levels.

Differential Privacy with KNG: A New Mechanism

Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.

Media Security Standards

Explores the definition of media security standards and the standardization of a framework rather than a specific security tool.

Cloud and Aggregation: Security and Privacy

Explores cloud computing, data aggregation, security risks, privacy defenses, and data protection techniques.

Technologies for Identity & Anonymity

Explores technologies for identity and anonymity, including e-ID, social media identities, and cryptographic signatures.

Anonymous Communications: Technologies and Challenges

Explores the challenges and technologies of anonymous communications, emphasizing the importance of privacy in modern communication systems.

Machine Learning and Privacy

Explores machine learning fundamentals and privacy risks, focusing on privacy attacks and collaborative machine learning.

Privacy: Why is privacy important

Explores the importance of privacy in democratic societies and the shared security infrastructure across individuals, industry, and governments.

Privacy: Differential Privacy & Privacy by Design

Explores differential privacy and Privacy by Design principles to ensure user-centric privacy protection.

Data Privacy in eHealth Research

Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.