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The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. However, using a single system allows an attacker only to perform one successful attack to corrupt the whole system. We present a new approach in which a data validation system using multiple different validators minimizes the probability of success for the attacker. The validators are arranged in clusters based on their properties. For a validation process, a challenge is given that specifies which validators should perform the current validation. Validation results from other validators are dropped. This ensures that even for more than half of the validators being corrupted anomalies can be detected during the validation process.
Testing applications for SmartHome environments is quite complicated, since a real environment is not accessible or the conditions are not controllable during development time. The need to set up the whole hardware environment, increase the complexity of these systems enormously. Therefore, it is helpful to simulate the SmartHome hardware components and environment conditions (e.g. rain, heat, etc.). This paper contains an approach to improve the test and demonstration process of Internet of Things scenarios. A prototype (ScnSim: Scenario Simulator) was developed to set up scenarios. The user of the ScnSim can create her/his own scenario using items (sensors/actuators) and rules, which control the sensors and actors building the IoT enviornment. This simulator is supposed to support the user testing IoT applications or configurations of SmartHome platforms like openHAB. In addition, the ScnSim is supposed to help demonstrating showcases, for example, often demonstrated on a trade fair or as a proof of concept for a customer.