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Improvements and Applications of Homomorphic Encryption.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Improvements and Applications of Homomorphic Encryption./
作者:
Alkharji, Majedah.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
182 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:
Dissertations Abstracts International82-08B.
標題:
Computer science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28314183
ISBN:
9798569978021
Improvements and Applications of Homomorphic Encryption.
Alkharji, Majedah.
Improvements and Applications of Homomorphic Encryption.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 182 p.
Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Thesis (Ph.D.)--The Catholic University of America, 2021.
This item must not be sold to any third party vendors.
Significant security issues arise when organizations outsource their data processing and computation tasks to cloud platforms. Homomorphic encryption (HE) provides a promising solution to address security threats because it allows performing mathematical operations such as addition and multiplication on encrypted data instead of plaintexts to obtain the same results. Users can upload HE encrypted data to the cloud, have the cloud computing complete computation on the ciphertexts, and then decrypt the processed ciphertexts to obtain the results. A privacy-preserving data processing (PPDP) system was recently proposed, which extends HE from a single-user system to a multi-user one by employing a ciphertext re-encryption technique to allow multiple users to access the processed ciphertexts. However, the size of HE encrypted data is much larger than the original data size, which causes a high bandwidth to transmit the ciphertexts to the cloud. In addition, homomorphic encryption introduces high computation complexity at the user devices. These user devices could be mobile or Internet of Things devices with low computation power. In this dissertation, we propose a multi-user Compact Privacy-Preserving Data Processing (CPPDP) system, which allows a user to upload the ciphertexts encrypted by the symmetric cryptographic algorithm such as Data Encryption Standard (DES) algorithm, to the cloud. The cloud parties perform the homomorphic encryption on DES ciphertexts and then DES decryption without disclosing the original information. CPPDP significantly reduces computation complexity at user devices and the bandwidth requirements between the user devices and the cloud. It can support mathematical computations, including Addition, Subtraction, and Multiplication over ciphertexts through the cooperation of the cloud Data Service Provider (DSP) and the Access Control Server (ACS). Moreover, the Genetic Algorithm (GA) based random number generator is designed to produce random keys. We have proved the security of the proposed CPPDP scheme. The performance and effectiveness of CPPDP have been analyzed and compared with the state-of-the-art PPDP scheme through simulations. The evaluation results show that CPPDP achieves the same security and computations performance while significantly lowering the users' data encryption complexity. CPPDP also greatly reduces the bandwidth when transmitting encrypted data to the cloud as well as the storage requirement when storing the ciphertexts in the cloud. A CPPDP-based cloud database for storing and processing the employees' data belonging to a university is implemented for the proof of concept, which enhances the data security and flexibility to access and analyze the data in the cloud environment.
ISBN: 9798569978021Subjects--Topical Terms:
523869
Computer science.
Subjects--Index Terms:
Cloud application
Improvements and Applications of Homomorphic Encryption.
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Significant security issues arise when organizations outsource their data processing and computation tasks to cloud platforms. Homomorphic encryption (HE) provides a promising solution to address security threats because it allows performing mathematical operations such as addition and multiplication on encrypted data instead of plaintexts to obtain the same results. Users can upload HE encrypted data to the cloud, have the cloud computing complete computation on the ciphertexts, and then decrypt the processed ciphertexts to obtain the results. A privacy-preserving data processing (PPDP) system was recently proposed, which extends HE from a single-user system to a multi-user one by employing a ciphertext re-encryption technique to allow multiple users to access the processed ciphertexts. However, the size of HE encrypted data is much larger than the original data size, which causes a high bandwidth to transmit the ciphertexts to the cloud. In addition, homomorphic encryption introduces high computation complexity at the user devices. These user devices could be mobile or Internet of Things devices with low computation power. In this dissertation, we propose a multi-user Compact Privacy-Preserving Data Processing (CPPDP) system, which allows a user to upload the ciphertexts encrypted by the symmetric cryptographic algorithm such as Data Encryption Standard (DES) algorithm, to the cloud. The cloud parties perform the homomorphic encryption on DES ciphertexts and then DES decryption without disclosing the original information. CPPDP significantly reduces computation complexity at user devices and the bandwidth requirements between the user devices and the cloud. It can support mathematical computations, including Addition, Subtraction, and Multiplication over ciphertexts through the cooperation of the cloud Data Service Provider (DSP) and the Access Control Server (ACS). Moreover, the Genetic Algorithm (GA) based random number generator is designed to produce random keys. We have proved the security of the proposed CPPDP scheme. The performance and effectiveness of CPPDP have been analyzed and compared with the state-of-the-art PPDP scheme through simulations. The evaluation results show that CPPDP achieves the same security and computations performance while significantly lowering the users' data encryption complexity. CPPDP also greatly reduces the bandwidth when transmitting encrypted data to the cloud as well as the storage requirement when storing the ciphertexts in the cloud. A CPPDP-based cloud database for storing and processing the employees' data belonging to a university is implemented for the proof of concept, which enhances the data security and flexibility to access and analyze the data in the cloud environment.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28314183
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