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Lecture

Privacy-Preserving Machine Learning

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.

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Instructors (2)

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.

Carmela González Troncoso

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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).

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.

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.

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.

Privacy law

Privacy law is the body of law that deals with the regulating, storing, and using of personally identifiable information, personal healthcare information, and financial information of individuals, which can be collected by governments, public or private organisations, or other individuals. It also applies in the commercial sector to things like trade secrets and the liability that directors, officers, and employees have when handing sensitive information.

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