The amount of data individuals create keeps increasing every year to the point that the data cannot be stored on a single device anymore. Cloud storage provides a solution for this problem, but not everybody wants the cloud storage service providers to peek at their data and they thus encrypt their data before storing it on the service provider's servers. Unfortunately, due to the way encryption works, the users are not able to perform simple actions on their data, like for example keyword search. However with Searchable Symmetric Encryption (SSE) the users can still perform keyword search on their data when their data is encrypted. With the use of SSE, there is some information that is being exposed about the data that is being stored on the system, called leakage. This leakage can be used by attackers in an attack to perform query recovery.

Currently existing attacks are mostly known-data attacks which assume that the attacker already has access to a large part of the plaintexts stored on the system. However this is very unlikely in real-world scenarios. A few papers focus on similar-data attacks which have a slightly different assumption. With similar-data attacks, the assumption is that the attacker has a similar document set to the document set stored on the SSE system. These attacks are therefore more realistic than known-data attacks, but the best similar-data attack still has some flaws.

Therefore, in this thesis, we propose a new attack that is based on an already existing similar-data query recovery attack. This new attack is a combination of a file injection attack and a similar-data attack. This new attack achieves a higher accuracy than the best similar-data and known-data attacks, while injecting only a few files into the SSE system. To the best of our knowledge this is the first similar-data attack with a file injection component. The new attack is also more resilient to countermeasures such as padding and obfuscation.

As the population increases so does the waste that is generated. Manually recycling waste is expensive and slow. Computer Vision (CV) solutions aim to make this less expensive and faster. Lots of data of this waste (thousands of images) is needed to train these CV solutions. This project, called Synthetic Waste Generator (SWaG) can create synthetic waste data through the use of Blender and Python. Moreover, this project makes a contribution to the current state of research by having developed an automated synthetic data generation pipeline. This synthetic data can be used to train CV solutions to enable automated recycling procedures. With the help of adjustable parameters, the synthetic data can be customized, such that different unique images of waste can be created deterministically based on a seed. Furthermore, SWaG is fully portable as it has been containerized using Docker which makes it extremely easy to obtain even faster results by running SWaG on an NVIDIA GPU enabled system as a single local container, on the cloud as a farm or incorporate it in a container-orchestration system such as Kubernetes. SWaG also crushes 3D models, to mimic real waste using soft body dynamics. The pipeline has also been suited to automatically generate COCO datasets by using masking and image segmentation techniques. SWaG can also add textures and different colors to the waste objects in the synthetically created image. Furthermore, with SWaG different conveyor belt setups at recycling plants can be simulated with the help of variable camera heights, conveyor belts, backgrounds and lighting conditions. SWaG is currently deployable and is being used and built upon by our client. After conducting empirical research experiments with SWaG, it is noted that its performance speed is linear as the amount of objects that are in a given scene increases. In fact, with between roughly 40 and 80 objects SWaG performs sub-linearly. This is an important performance criteria as images of trash on the conveyor belt often have tonnes of objects pilled up on top of one another.