A searchable symmetric encryption (SSE) scheme allows a user to securely perform a keyword search on an encrypted database. This search capability is useful but comes with the price of unintentional information leakage. An attacker abuses leakage to steal confidential information by launching SSE attacks. In this work, our goal is to design a new inference attack that improves the query recovery accuracy of an existing attack. We combine an additional volume leakage pattern and investigate the effectiveness of existing countermeasures against it. Our attack utilizes similar data knowledge and known queries to perform the attack. The results show that usage of an additional volume leakage pattern results in an improved query recovery accuracy, and a more stabilized spread in the results. When an attacker knows up to 4 known queries, we observe an improved query recovery accuracy between 5 and 19.5%. Furthermore, we investigate if the attack can be improved even further by utilizing clustering. However, the results are too close with a high trade-off in performance. From our findings, we can generalize that additional knowledge available to the attacker improves query recovery accuracy. More leakage combinations and their impact are open to future research.

Escape rooms are an emerging market. In these physical and mostly group based games, players are locked in a room with the goal to solve puzzles within a certain time limit in order to ’escape’ the room. To make sure players have a pleasurable experience, they are monitored during the game by a so called game master. This person helps players during the game. When players get stuck the game master gives players a hint. Another possible task of the game master is to create an immersive story within the escape room. The game master can do this by communicating with the players while taking on the role of certain characters that are part of that story.
The goal of this project was to develop the Escape-Master system: a system that automates the role of the game master. In the system the game master can define an arbitrary escape room by inputting puzzles, characters and dialog for those characters. This dialog can include hints or storyline narrative. With this information provided, the system can automatically interact with the players. The system keeps track of the progress of the players and uses this information to interpret questions they provide to the system using natural language processing. After this the system can provide a fitting response, which can be a hint or narrative that progresses the story of the escape room. The system can also start interactions with players on its own, based on time passed of their progress in the game.
The project consisted of two phases: research and development. During the research phase information was gathered on player interaction, user engagement, and chatbots. During the development phase a system was constructed that permits the game master automate the process of giving hints and create an immersive story. Additionally, an interface was build to give the game master the ability to define an escape room and start games while being notified of the actions the system takes. It was tested in a live setting on two occasions, which yielded favorable results in terms of user engagement and immersion.