Trusted Data Management
Classical data management systems mainly try to improve the efficiency and scalability of storing and processing data. Our projects in the area of Trusted Data Management introduce a new perspective: A data management systems should provide users with additional trust guarantees that increase the confidence of the user while interacting with the system itself and with other users through the system.
Such guarantees include the security to prevent unauthorized access and manipulation of the stored data by a user, or even the administrator. Moreover, the explainability of results is an important aspect, since it increases the confidence of the user that the system indeed returned a correct and integer (query) result.
In the following, you can find a selection of active and past projects at our lab in this exciting research area:
BlockchainDB is a new trusted DBMS that leverages blockchains as the native storage layer and implements an additional database layer on top to enable access to data stored in so called shared tables. Shared tables allow multiple distrustful parties to collaboratively maintain and query data.
Decentralized Federated ML
Decentralized Federated Machine Learning (ML) aims at enabling a collaborative machine learning setup in which parties that do not trust each other can work together to learn powerful ML models. Decentralized Federated ML uses Blockchain technology to decentralize previously centralized components in a ML setup and introduces novel protocols that prevent participants from manipulating the learning procedure or the resulting model.