The increasing reliance on cloud computing has transformed how organizations store and process their data, offering huge scalability and cost-efficiency. However, such a shift has raised significant concerns regarding data security and privacy, especially when sensitive information is handled by third-party providers. For that reason, robust encryption techniques are of primary importance for safeguarding sensitive data. Despite the necessity of encryption, it is observed that most encryption techniques complicate the execution of complex data queries such as skyline queries, which in turn are essential for decision making in multi-criteria problems. This master thesis explores the problem of efficiently and securely performing skyline queries over location-based encrypted data. Specifically, it evaluates existing encryption schemes, assessing their impact on both the security and performance of skyline query computations. Through a detailed comparative analysis, this work aims to identify the most effective computation techniques existing in the related literature that balance security with computational efficiency, ultimately providing insights into how to secure databases while enabling fast and accurate query processing in cloud environments.
Νικόλαος Α. Μικρού (Wed,) studied this question.