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In the Master’s thesis were described privacy protection problems while using cloud technologies. Some of the problems can be solved with help of homomorphic encryption, data splitting or searchable encryption. These techniques were described and compared by provided security, privacy protection and efficiency. The data splitting technique was chosen and implemented in the C language. Afterwards a performance of the implemented solution was compared to AES encryption/decryption performance. An application for secured data storing in cloud was designed and implemented. This application is using the implemented data splitting technique and third-party application CloudCross. The designed application provides command line interface (CLI) and graphical user interface (GUI). GUI extends the capabilities of CLI with an ability to register cloud and with an autodetection of registered clouds. The process of uploading/downloading the data to/from cloud storage is transparent and it does not overload the user with technical details of used data splitting technique.

In the Master’s thesis were described privacy protection problems while using cloud technologies. Some of the problems can be solved with help of homomorphic encryption, data splitting or searchable encryption. These techniques were described and compared by provided security, privacy protection and efficiency. The data splitting technique was chosen and implemented in the C language. Afterwards a performance of the implemented solution was compared to AES encryption/decryption performance. An application for secured data storing in cloud was designed and implemented. This application is using the implemented data splitting technique and third-party application CloudCross. The designed application provides command line interface (CLI) and graphical user interface (GUI). GUI extends the capabilities of CLI with an ability to register cloud and with an autodetection of registered clouds. The process of uploading/downloading the data to/from cloud storage is transparent and it does not overload the user with technical details of used data splitting technique.

In the Master’s thesis were described privacy protection problems while using cloud technologies. Some of the problems can be solved with help of homomorphic encryption, data splitting or searchable encryption. These techniques were described and compared by provided security, privacy protection and efficiency. The data splitting technique was chosen and implemented in the C language. Afterwards a performance of the implemented solution was compared to AES encryption/decryption performance. An application for secured data storing in cloud was designed and implemented. This application is using the implemented data splitting technique and third-party application CloudCross. The designed application provides command line interface (CLI) and graphical user interface (GUI). GUI extends the capabilities of CLI with an ability to register cloud and with an autodetection of registered clouds. The process of uploading/downloading the data to/from cloud storage is transparent and it does not overload the user with technical details of used data splitting technique.

Bezpečnost a zachování důvěrnosti dat uložených s využitím veřejných cloudových služeb je dnes velmi aktuální téma. V této práci se, na rozdíl od tradičního šifrování, zabýváme možností využití dělení dat k zabezpečení citlivých dat. Cílem práce je implementovat a porovnat dva již publikované protokoly pro násobení matic využívající dva rozdílné přístupy dělení dat. Na základě jejich vlastností byla navržena originální varianta, která na rozdíl od původních protokolů zohledňuje i případ, kdy poskytovatelé cloudových slu- žeb tajně spolupracují. Všechny tyto protokoly byly naimplementovány za použití plat- formy Java a veřejných cloudových služeb. Na závěr byla změřena výkonnostní náročnost a zhodnoceny požadavky na úložiště.

Data splitting is commonly used in machine learning to split data into a train, test, or validation set. This approach allows us to find the model hyper-parameter and also estimate the generalization performance. In this research, we conducted a comparative analysis of different data partitioning algorithms on both real and simulated data. Our main objective was to address the question of how the choice of data splitting algorithm can improve the estimation of the generalization performance. Data splitting algorithms used in this study were variants of k-fold, Kennard-Stone, SPXY ( sample set partitioning based on joint x-y distance), and random sampling algorithm. Each algorithm divided the data into two subset, training/validation. The training set was used to fit the model and validation for the evaluation. We then analyzed the different data splitting algorithms based on the generalization performances estimated from the validation and the external test set. From the result, we noted that the important determinant for a good generalization is the size of the dataset. For all the data sample methods applied on small data set, the gap between the performance estimated on the validation and test set was significant. However, we noted that the gap reduced when there was more data in training or validation. Too many or few data in the training set can also lead to bad model performance. So it is importance to have a reasonable balance between the training/validation set sizes. In our study, KS and SPXY was the splitting algorithm with poor model performance estimation. Indeed these methods select the most representative samples to train the model, and poor representative samples are left for model performance estimation. / Datapartitionering används vanligtvis i maskininlärning för att dela data i en tränings, test eller valideringsuppsättning. Detta tillvägagångssätt gör det möjligt för oss att hitta hyperparametrar för modellen och även uppskatta generaliseringsprestanda. I denna forskning genomförde vi en jämförande analys av olika datapartitionsalgoritmer på både verkliga och simulerade data. Vårt huvudmål var att undersöka frågan om hur valet avdatapartitioneringsalgoritm kan förbättra uppskattningen av generaliseringsprestanda. Datapartitioneringsalgoritmer som användes i denna studie var varianter av k-faldig korsvalidering, Kennard-Stone (KS), SPXY (partitionering baserat på gemensamt x-y-avstånd) och bootstrap-algoritm. Varje algoritm användes för att dela upp data i två olika datamängder: tränings- och valideringsdata. Vi analyserade sedan de olika datapartitioneringsalgoritmerna baserat på generaliseringsprestanda uppskattade från valideringen och den externa testuppsättningen. Från resultatet noterade vi att det avgörande för en bra generalisering är storleken på data. För alla datapartitioneringsalgoritmer som använts på små datamängder var klyftan mellan prestanda uppskattad på valideringen och testuppsättningen betydande. Vi noterade emellertid att gapet minskade när det fanns mer data för träning eller validering. För mycket eller för litet data i träningsuppsättningen kan också leda till dålig prestanda. Detta belyser vikten av att ha en korrekt balans mellan storlekarna på tränings- och valideringsmängderna. I vår studie var KS och SPXY de algoritmer med sämst prestanda. Dessa metoder väljer de mest representativa instanserna för att träna modellen, och icke-representativa instanser lämnas för uppskattning av modellprestanda.