Lecture

Privacy-Preserving Machine Learning

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.

In course

CS-523: Advanced topics on privacy enhancing technologies

This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evaluate the protection they provide. ...

Description

This lecture covers techniques such as Additive and Shamir Secret Sharing for protecting secrets and enabling computations without revealing sensitive data. It explores operations on secret shared data, including secure multiparty computation protocols. The lecture delves into the combination of Homomorphic Encryption with secret sharing schemes, addressing the challenges of communication-intensive computations. It presents real-world applications in healthcare and discusses the envisioned nation-wide deployment of privacy-preserving techniques. The lecture concludes with a detailed overview of POSEIDON, a system for Privacy-Preserving Federated Neural Network Learning, highlighting its building blocks, evaluation, scalability, and the challenges addressed by the solution.

Instructors (2)

Carmela González Troncoso

SPRING Lab Our research focuses on building secure and privacy-preserving systems. We aim to develop tools and methodologies to help engineers embedding strong security and privacy guarantees in their designs, as well as techniques that help them to systematically quantifying the information that an adversary can infer from data she may have access to.More information about our activities here: https://spring.epfl.ch/ Teaching & PhD PhD Students Saiid El Hajj Chehade, Eric Jollès, Christian Knabenhans, Boy

Alessandro Chiesa

Alessandro Chiesa is a faculty member in computer science. He conducts research in complexity theory, cryptography, and security, with a focus on the theoretical foundations and practical implementations of cryptographic proofs that are short and easy to verify. He is a co-author of several zkSNARK libraries, and is a co-inventor of the Zerocash protocol. He has co-founded Zcash and StarkWare Industries. He is a recipient of a Sloan Research Fellowship (2021), an Okawa Foundation Research Grant (2020), and Google Faculty Research Awards (2018 and 2017). He was included in MIT Technology Review's "35 Innovators Under 35" list in 2018.

Official source

Related concepts (32)

Secret sharing

Secret sharing (also called secret splitting) refers to methods for distributing a secret among a group, in such a way that no individual holds any intelligible information about the secret, but when a sufficient number of individuals combine their 'shares', the secret may be reconstructed. Whereas insecure secret sharing allows an attacker to gain more information with each share, secure secret sharing is 'all or nothing' (where 'all' means the necessary number of shares).

Shamir's secret sharing

Shamir's secret sharing (SSS) is an efficient secret sharing algorithm for distributing private information (the "secret") among a group so that the secret cannot be revealed unless a quorum of the group acts together to pool their knowledge. To achieve this, the secret is mathematically divided into parts (the "shares") from which the secret can be reassembled only when a sufficient number of shares are combined.

Secure multi-party computation

Secure multi-party computation (also known as secure computation, multi-party computation (MPC) or privacy-preserving computation) is a subfield of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. Unlike traditional cryptographic tasks, where cryptography assures security and integrity of communication or storage and the adversary is outside the system of participants (an eavesdropper on the sender and receiver), the cryptography in this model protects participants' privacy from each other.

Data Protection Directive

The Data Protection Directive, officially Directive 95/46/EC, enacted in October 1995, was a European Union directive which regulated the processing of personal data within the European Union (EU) and the free movement of such data. The Data Protection Directive was an important component of EU privacy and human rights law. The principles set out in the Data Protection Directive were aimed at the protection of fundamental rights and freedoms in the processing of personal data.

Information privacy

Information privacy is the relationship between the collection and dissemination of data, technology, the public expectation of privacy, contextual information norms, and the legal and political issues surrounding them. It is also known as data privacy or data protection. Data privacy is challenging since attempts to use data while protecting an individual's privacy preferences and personally identifiable information. The fields of computer security, data security, and information security all design and use software, hardware, and human resources to address this issue.

Related lectures (64)

Privacy Technologies and Data Protection

Explores privacy technologies, data protection, surveillance risks, and Privacy Enhancing Technologies for social and institutional privacy.

Homomorphic Encryption: Delegating Calculations Securely

Explores homomorphic encryption, enabling secure computations on encrypted data without data exposure, with applications in healthcare and finance.

Privacy-Preserving Federated Analytics for Personalized Medicine

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

Privacy technologies and data protection

Explores privacy importance, PETS, anti-surveillance technologies, and the SwissCovid case study.

Privacy: Threat Modeling and Differential Privacy