Searchable Symmetric Encryption (SSE) schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access, search, and volume pattern. In most existing Searchable Encryption (SE) schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Many attacks on SSE schemes have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Each attack abuses a different leakage pattern and uses different techniques to achieve high query recovery accuracy. An attacker could be passive or active, where an active attacker can inject files in an SSE scheme, while a passive attacker only observes the queried data. Some passive attacks use the number of files returned by a query to create a match with a candidate keyword. Others use the co-occurrence of multiple keywords in the files to match a query with the same occurrence. We continue this research and design a new Volume and Access Pattern Leakage-abuse Attack (VAL-Attack) that exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs with great accuracy and without false positives. We compare VALAttack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures.

The FireFly company was created on the idea of blind dating. Most dating apps need the user to have an extensive conversation through a chat box before settling for a date. The product of FireFly tries to remedy that by having the first conversation be face to face. Over ten weeks, we have built the FireFly Dating application. We started researching other dating applications and matching algorithms to decide the requirements for FireFly Dating. After this, we implemented these requirements from scratch. In the final product, users can register an account, like or dislike other users profiles to indicate preferences for the matching algorithm, enroll for a blind date, and get matched. The application also includes an automated emailing system and a full reporting and feedback system. Furthermore, the system allows for an Administrator to get an overview of the application’s data. The Administrator can insert, edit or archive Dating Establishments and Time Slots. As well as view user reports and ban users if necessary. The final application is a product that portrays the Product Owner’s vision in that dating should be easily accessible and offline. At the end of the project, together with the Product Owner, it can be concluded that the FireFly Dating application was successful.

The monumental goal of Artificial Intelligence (AI) is to model general solutions that can be applied to perform a variety of tasks that normally demand human intelligence to solve. Traditionally human game developers painstakingly design and tweak levels until achieving the precise output of their heart’s desire. In the gaming industry, AI for level generation can reduce the need for labour-intensive human design. A general game AI for level generation can only be created once we have a method to describe video games. Video Game Description Language (VGDL) is a high-level language for describing 2D arcade games that consists of two parts, a game and level description. Using this language allows us to analyze games at their mechanical level. The problem of General Video Game Level Generation (GVG-LG) can thus be defined as follows: construct a generator that, given a game (e.g. described in VGDL) which can be played by some AI player, builds any required number of different levels for that game which are enjoyable for humans to play [1]. This research investigates the characteristics of what makes automation of general level generation for 2D video games difficult, identifying what exactly makes it so challenging. Solutions such as algorithmic approaches and design patterns shall be presented. By investigating the techniques that have been used so far, empirical evidence will provide key insight into which techniques are most promising to improve level generation quality in the future.