Janwillem Swalens

In stream processing, managing sensitive information in a timely manner while ensuring trust remains a significant challenge. When parties without a priori trust cooperate to execute a streaming application, it is difficult to ensure that sensitive data is kept confidential while guaranteeing that every party executes their code honestly. This paper presents zkStream: a framework that leverages signatures and zero-knowledge proofs (ZKP) to add trust to streaming applications that run in the edge cloud, guaranteeing data confidentiality, provenance, and computational integrity. We introduce two optimizations to minimize the computational overhead associated with ZKPs, making our framework suitable for real-world applications. We validated our solution with existing benchmarks for streaming applications. Our method achieves an end-to-end latency that is between 6.5 and 15× faster than a naive implementation, demonstrating its potential for industrial adoption where trust is critical.

Multiple inheritance is often criticised for the ambiguity that arises when multiple parents want to pass on a feature with the same name to their offspring. A survey of programming languages reveals that no programming language has an inherently implicit and dynamic approach to resolve this ambiguity. This paper identifies just-in-time inheritance as the first implicit and dynamic inheritance mechanism. The key idea of just-in-time inheritance is that one of the parents is favoured over the others, which resolves the ambiguity, and that the favoured parent can change at runtime. However, just-in-time inheritance is not the silver bullet to solve all ambiguity problems heir to multiple inheritance, because it is not applicable in all scenarios. We conclude that the applicability of just-in-time inheritance is to be found in systems where multiple inheritance is used to model an ``is-a OR is-a''-relation, rather than the more traditional ``is-a AND is-a''-relation.