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Harnessing the Power of Distributed Computing : = Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Harnessing the Power of Distributed Computing :/
其他題名:	Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security.
作者:	Tawose, Olamide Timothy.
面頁冊數:	1 online resource (135 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380367158

Harnessing the Power of Distributed Computing : = Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security.
Tawose, Olamide Timothy.

Harnessing the Power of Distributed Computing :Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security. - 1 online resource (135 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of Nevada, Reno, 2023.

Includes bibliographical references

Data explosion poses lot of challenges to the state-of-the art systems, applications, and methodologies. It has been reported that 181 zettabytes of data are expected to be generated in 2025 which is over 150% increase compared to the data that is expected to be generated in 2023. However, while system manufacturers are consistently developing devices with larger storage spaces and providing alternative storage capacities in the cloud at affordable rates, another key challenge experienced is how to effectively process the fraction of large scale of stored data in time-critical conventional systems. One transformative paradigm revolutionizing the processing and management of these large data is distributed computing whose application requires deep understanding. This dissertation focuses on exploring the potential impact of applying efficient distributed computing concepts to long existing challenges or issues in (i) a widely data-intensive scientific application (ii) applying homomorphic encryption to data intensive workloads found in outsourced databases and (iii) security of tokenized incentive mechanism for Federated learning (FL) systems.The first part of the dissertation tackles the Microelectrode arrays (MEAs) parameterization problem from an orthogonal viewpoint enlightened by algebraic topology, which allows us to algebraically parametrize MEAs whose structure and intrinsic parallelism are hard to identify otherwise. We implement a new paradigm, namely Parma, to demonstrate the effectiveness of the proposed approach and report how it outperforms the state-of-the-practice in time, scalability, and memory usage.The second part discusses our work on introducing the concept of parallel caching of secure aggregation to mitigate the performance overhead incurred by the HE module in outsourced databases. The key idea of this optimization approach is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. A new radix HE algorithm was designed and applied to both batch and incremental HE schemes, and experiments carried out on six workloads show that the proposed caching boost state-of-the-art HE schemes by high orders of magnitudes.In the third part, I will discuss our work on leveraging the security benefit of blockchains to enhance or protect the fairness and reliability of tokenized incentive mechanism for FL systems. We designed a blockchain-based auditing protocol to mitigate Gaussian attacks and carried out experiments with multiple FL aggregation algorithms, popular data sets and a variety of scales to validate its effectiveness.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380367158Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Big dataIndex Terms--Genre/Form:

554714
Electronic books.

Harnessing the Power of Distributed Computing : = Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security.
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