The College of Education for Pure Sciences in the Department of Computer Science at the University of Basrah  discussed a master’s thesis on developing searchable homomorphic encryption performance on files by improving locality.
The message presented by the researcher (Aya Abdel Hussein Abdel Razzaq) included that the world continues to develop rapidly, and the volume of data is witnessing an amazing increase. This tremendous growth underscores the growing demand for cloud storage among businesses, individuals, and data owners. Hence, ensuring the security of data stored in cloud servers has become an issue of utmost importance to many. In this context, Searchable Symmetric Encryption (SSE) is one useful solution to this issue. SSE is a strong encryption method that allows users to store and retrieve encrypted data on a remote server such as a cloud server while keeping user data private. However, previous research in this area revealed that SSE experiences a clear weakness in performance when dealing with large databases. The issue is due to poor locality, a condition that requires cloud servers to access multiple memory locations to process just one query. In addition, previous endeavors in this area that focused on optimizing locality often lead to expanded storage requirements (the stored encrypted index should not be much larger than the original index) or diminished data read efficiency (only the required data should be retrieved). We have designed three simple, secure, searchable, and cost-effective schemes to address the weak locality problem. Our schemes mainly improve information retrieval performance by improving locality by changing the inverted index storage mechanism. The proposed schemes offer optimal localityO(1), which means that the cloud server only needs to access a single memory location instead of multiple locations. In addition, they provide the best O(1) read efficiency, which means that the cloud server retrieves only the required data. The local optimizations we make in our schemes do not increase storage space. On the contrary, the storage space is improved for the better. Furthermore, we conduct comprehensive security analyzes to evaluate our system's vulnerability to data leakage. Our schemes exhibit minimal leakage types and demonstrate their robustness against various important attacks including frequency analysis attack, keyword guessing attack, and man-in-the-middle attack. Additionally, using real-world data, we demonstrate that our schemes are secure, practical, efficient, and highly accurate

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