Huge amounts of data exist about every one of us, the use of which has the potential to improve our lives and the world we live in. However, concerns about the privacy of this data have naturally become an increasingly prevalent issue. The aim of privacy-preserving analysis is to utilise this data to its fullest potential without compromising our privacy.
What’s the aim?
To understand the interplay between different privacy-enhancing techniques and how they can be used in practice for privacy-preserving data analysis.
It is important to develop a unified approach to secure, privacy-preserving data analysis as well as finding an effective, mathematically robust definition of privacy.
We will organise periodic workshops and talks at the Turing, as well as lectures and tutorials aimed at a general audience. Although the focus of the group is on technical aspects, engaging with researchers on ethical and regulatory aspects will be one of the workshops’ goals.
- Privacy-preserving data analysis has become a crucial aspect of data science, and is recognised as an important problem within several research communities.
- Recent advances in cryptography, systems, and hardware security, have made privacy-preserving computation practical.
- There are several deployments in existing and new products, and lots of interest both from industry and the government.
Challenges: Technical issues, security breaches, human errors or scalabilityExamples: Making health data accessible to researchers
Challenges: Privacy concernsExamples: Joining data from two medical organisations to produce more accurate analysis
Challenges: A cryptographic approach or a hardware based approach, or a combinationExamples: Leveraging cloud infrastructure to free organisations from having to maintain their own secure data centres
Challenges: Avoiding storing particular individual’s data in a central server, avoiding re-identificationExamples: Computing aggregate statistics from user data collected from mobile devices or internet browsers
Disciplines & Techniques
Cryptography | Statistics | Machine learning | Systems/hardware security | Formal methods
Efficient homomorphic encryption | Differential privacy | Multi‑party computation | Secure enclaves | Cryptographic verifiable computation