Homomorphic encryption is an encryption scheme that allows for simple operations on encrypted data. These operations are mainly boolean circuits combined into more complexarithmetic operations, rotations, and others. Homomorphic encryption was first implemented in 2009, and in the following decade, many different versions emerged. The early schemes were mainly proof of concepts. In contrast, the later schemes have been used in practical applications such as databases where queries were done without any decryption on the server. Another practical example is genome sequencing which benefits from utilizing supercomputers but the data is very sensitive. With the help of homomorphic encryption it was shown that this could be done without having any unencrypted data on the server. While these applications have different success rates, a field that have not been investigated is the use of homomorphic encryption with delta encoding. Delta encoding is a method of encoding a set (e.g., a set of characters) such that the set is expressed as an original (a starting point) with deltas (changes). A typical use case for delta encoding is: A user wants to edit a file located on the cloud and to save bandwidth, the user could encode a delta locally. This delta could then be sent to the cloud service and decoded together with the original version to create the updated version on the cloud. However, there is a privacy infringement risk with this. When standard encryption is used, the delta and the original must be decrypted to perform the decoding. If a malicious actor gains access to the data on the cloud machine, they would then have access to unencrypted data. For example, the cloud provider could snoop on its customers or have a policy that lets them use the users’ data. Homomorphic encryption would make it much harder since the data would still be encrypted while the decoding is performed. However, homomorphic encryption comes with a great overhead and is complex to tune, even with today’s libraries.To investigate the combination of homomorphic encryption and delta encoding, a testbed is created where a client and server act as user and cloud provider. Thetest bed consists of different configurations of delta encodings and homomorphic encryption schemes running different test cases. The configurations range from non-encrypted to homomorphically encrypted with different kinds of delta encodings to investigate the performance overhead of utilizing homomorphic encryption. The different tests are created to show what kind of overhead can be expected in different scenarios and which operations take the most time. With this testbed and these test cases, the results showed a substantial overhead with using homomorphic encryption. However, many optimizations could be done to increase efficiency and make homomorphic encryption a viable solution. For example, the decoding algorithm could be optimized to use homomorphic operations more efficiently. The tests showed that most of the runtime, when using homomorphic encryption, is on the server. Most of the runtime for the client are one-time operations, which consist of creating keys that can be reused.

標準化的地理資料交換格式是開放式地理資訊系統不可或缺的一環，可提供不同的平台經由統一的交換格式而順利進行資料交換。GeoJSON除了具備基本的資料互通性外，其結構簡單且容易讀取的特性為地理資訊服務軟體帶來許多效益。然而座標資料量之多寡直接影響資料傳輸效能，為提昇傳輸效率，必須配合有效的資料壓縮技術以降低傳輸之資料量。此外，GeoJSON壓縮方法的設計與結構訴求，也須融合GeoJSON的概念，以簡單、方便運用與容易了解為目標。 本研究提出一套GeoJSON壓縮技術進行空間資料的壓縮，以降低資料量而增進Web GIS地理資料傳輸的效能。除評估其壓縮效率，並透過不同規模以及不同型態的地理資料，分析影響壓縮效率的原因。最後藉由HTTP壓縮技術輔助，並從資料傳遞的時間與壓縮率，評估本研究提出的方法所帶來的成效。我們同時以單向雜湊函數，建立資料傳遞時的檢查機制，以確保資料傳遞時的正確性與一致性。 實作中，我們採用GeoJSON壓縮技術，進行座標資料的大小減量實驗，結果顯示本研究方法可以得到不錯的壓縮成果與傳輸效能，並且可避免資料傳輸發生問題。 / Standardized GIS data exchanging format is an essential part of Open GIS. This enables GIS data providers, software developers, and system integrators to exchange GIS data from different platform. The simple structure of GeoJSON not only has the data interoperability potential but also has the characteristics of easy processed and readability. These properties directly benefit the GIS service software. However, the amount of spatial information encoded in GIS documents usually has direct impact to the efficiency of GIS data transmission. In order to improve the data transmission efficiency, one has to reduce the amount of spatial data transmitted through data compression techniques. In this thesis, we proposed a data compression mechanism for spatial data. Our mechanism, co-operated with the concept of GeoJSON, aim at simple, easy to understand, and easy to use, can reduce the amount of spatial data transmitted and improve the transmission efficiency. We analyzed the compression ratio of various data types and data amounts through different base parameters. We also measured the system response time reduced using this method and compared with the combination of using our method as well as the HTTP compression modules. A one-way hashing technique is used to ensure data accuracy and consistency during the transmission processes. The experimental results show that our GeoJSON-based compression mechanism can significantly reduce the file size of spatial data and improve the efficiency of spatial data transmission. In addition, the data communication errors can be avoided.

Cloud storage services allow users to store and access data in a secure and flexible manner. In recent years, cloud storage services have seen rapid growth in popularity as well as in technological progress and hundreds of millions of users use these services to store thousands of petabytes of data. Additionally, the synchronization of data that is essential for these types of services stands for a significant amount of the total internet traffic. In this thesis, seven cloud storage applications were tested under controlled experiments during the synchronization process to determine feature support and measure performance metrics. Special focus was put on comparing applications that perform client side encryption of user data to applicationsthat do not. The results show a great variation in feature support and performance between the different applications and that client side encryption introduces some limitations to other features but that it does not necessarily impact performance negatively. The results provide insights and enhances the understanding of the advantages and disadvantages that come with certain design choices of cloud storage applications. These insights will help future technological development of cloud storage services.