QIF

We will give a high-level presentation of some recent works in Quantitative Information Flow which have brought results of interest for the wider communities of Information Theory and Quantum security. The first work is based on the notion of core-concavity, and it allows to generalize some axiomatic approach for Quantitative Information Flow to an axiomatic presentation of Information Theory for general entropy functions. Several fundamental Information Theoretical results can be proven in this framework. (Joint work with Arman Khouzani and Arthur Americo) The second contribution introduces quantum quantitative information flow, a leakage framework for quantum computation. The framework is a natural extension of gain functions and is based on Buscemi’s work on the theory of statistical comparison in quantum information, theory for which a quantum equivalent to the coriaceous theorem holds. (Joint work with Arthur Americo)

In Brazil, the issues of privacy and transparency in the release of official statistical data are governed by two complementary laws. On the one hand, a privacy law enacted in 2018, known as LGPD, (and heavily based on the European General Data Protection Regulation), establishes restrictions on governmental agencies that publicly release data on individuals, and prescribes sanctions in case of non-compliance.

With the introduction of new privacy laws across many countries, there is an expectation that programmers will be equipped with the knowledge and tools to implement the principles of privacy-by-design. An important feature of privacy laws such as GDPR is that they permit the release of datasets (and derived products) so long as they are reasonably de-identified.

In this talk I will introduce the QIF approach to studying secure systems, focussing on the notion of refinement which provides a robust method of comparing systems wrt Bayesian threats. I will introduce a variety of new refinement orders inspired by QIF and DP which allow us to study threats to privacy using max-case notions. We will see how to apply these orders to the task of comparing DP mechanisms, raising the question of whether the order based on epsilon provides strong privacy guarantees.

In this talk I will outline how the theory of Quantitative Information Flow which was developed to provide a rigorous framework to measure information leakage was used to probe the Brazilian Education Censuses. Using this theory applied to these datasets showed that the defences proposed by the Ministry were prone to serious vulnerabilities, in some cases leading to complete re-identification under reasonably modest attacks. The consequence is that our work has provided the agency with rigorously formalized risk analysis, which will help it make informed decisions.

In this talk I will describe some recent developments concerning how to measure the severity of information leaks based on new definitions of entropy, and how to apply them to programming language semantics. The ideas are illustrated by measuring the degree of privacy participants can rely on when they vote using some standard voting protocols.