Pille Pullonen-Raudvere

Multi-point function secret sharing (FSS) is a building block for pseudo-random correlation generators used in novel silent correlation generation methods for various secure multi-party computation applications. However, the main construction used so far is the naive approach to combining several point functions. In this paper, we propose an efficient and natural generalisation of the point function FSS scheme of Boyle et al. 2016 using a tree structure, a pseudorandom generator and systems of linear equations. We propose a new notion of distributed random multi-point function. Our construction splits the distributed multi-point function scheme into a random multi-point function scheme and an algorithm to transform a random output into the desired output value. The resulting scheme, which we call SLAMP-FSS, improves upon the state of the art in terms of calls to a pseudorandom generator (PRG).

Software services play a crucial role in daily life, with automated actions determining access to resources and information. Trusting service providers to perform these actions fairly and accurately is essential, yet challenging for users to verify. Even with publicly available codebases, the rapid pace of development and the complexity of modern deployments hinder the understanding and evaluation of service actions, including for experts. Hence, current trust models rely heavily on the assumption that service providers follow best practices and adhere to laws and regulations, which is increasingly impractical and risky, leading to undetected flaws and data leaks. In this paper, we argue that gathering verifiable evidence during software development and operations is needed for creating a new trust model. Therefore, we present TrustOps, an approach for continuously collecting verifiable evidence in all phases of the software life cycle, relying on and combining already existing tools and trust-enhancing technologies to do so. For this, we introduce the adaptable core principles of TrustOps and provide a roadmap for future research and development.

This paper formalises the security of programmable secure computation focusing on simplifying security proofs of new algorithms for existing computation frameworks. Security of the frameworks is usually well established but the security proofs of the algorithms are often more intuitive than rigorous. This work specifies a transformation from the usual hybrid execution model to an abstract model that is closer to the intuition. We establish various preconditions that are satisfied by natural secure computation frameworks and protocols, thus showing that mostly the intuitive proofs suffice. More elaborate protocols might still need additional proof details.

In a distributed scenario it is possible to find systems consisting of independent parties that collaboratively execute a business process, but cannot disclose a subset of the data used in this process to each other. Such systems can be modelled using the PE-BPMN notation: a privacy-enhanced extension of the BPMN process modeling notation. Given a PE-BPMN model, we address the problem of verifying that the content of certain data objects is not leaked to unauthorized parties. To this end, we formalise the semantics of PE-BPMN collaboration diagrams via a translation into process algebraic specifications. This formalisation enables us to apply model checking to detect unintended data leakages in a PE-BPMN model. We specifically consider data leakages in the context of secret sharing technology. The approach has been implemented on top of the mCRL2 toolset, and integrated into the Pleak toolset supporting privacy analysis of business processes. The proposal has been evaluated using real scenarios.