Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of such protocols less likely.

We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on an asynchronous probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Our protocol is robust to user dropouts and is non-interactive apart from the registration phase. We describe several optional extensions to our protocol for temporal aggregation, dynamic user joins and leaves, and differential privacy. We analyse our protocol in terms of security, privacy, and detection rate. Finally, we compare the runtime complexity of our protocol with a selection of related protocols.

Library developers are often unaware of how their library is used exactly in practice. When a library developer changes the internals of a library, this may unintentionally affect or even break the working of the library users' code. While it is possible to detect when a syntactic breaking change occurs, it is not as easy to detect semantic breaking changes, where the implicit contract of a functionality changes, sometimes unbeknownst to the library developer. Because library users rarely test the behaviour they expect of the library, neither the library developer nor the library user will be aware of the new behaviour.

As a library developer, you want to be able to see how a change in your library will affect your users before a new version of the library is deployed. More specifically, you want to gain insight into how users use the library, and want to see if and how changes affect users. This will allow you to determine whether the new version of the library is backwards compatible. Finally, after deploying the breaking changes, you want to notify the affected users of the changes and of a solution to the issue.

Schaapi, a tool for early detection of breaking changes based on API usages, addresses these needs. It mines public repositories for projects using a given library, analyses their usage of the API of that library, and generates tests that capture this behaviour. Finally, it offers a continuous integration service that automatically executes these tests against new versions of the library and warns developers of any potentially breaking changes in functionality. The tool has also been validated against real-world data to demonstrate its performance in realistic usage scenarios and to answer a selection of related research questions.