Merkezi olmayan güvenlik ve verilerin kullanım blokzincirinin bütünlüğü öğrenme teknikleri

Blockchain technology has been linked with Deep Learning for a long time now. There are many issues that are hinder the implementation of deep learning applications at a large scale. Surveys and studies from multiple sources reveal that security threats and data privacy are still the primary concerns. These problems are well known and solutions exist for these problems in the IT industry. However, traditional IT security solutions cannot be applied to Deep learning for various reasons spanning from type of devices to sheer volume of devices. Unfortunately, like in any other industry, security is often disregarded in the deep learning domain as well, and most of the resources are allocated to application development and device hardware. So, the search for a silver bullet to overcome these inhibitors has been going on for a while. After Bitcoin became prominent, people started to realize the potential of the underlying distributed ledger (blockchain) technology and considered it as a true innovation. Rather than facilitating a peer-to-peer digital payment system involving a cryptocurrency, the blockchain technology is viewed as a mechanism that provides device identity, secure data transfer, and immutable data storage. All these features can be implemented without any centralized authority and a completely transparent system with auditable cryptographic proofs. Our aim through this research thesis is to get a deep level ABSTRACT DECENTRALIZED SECURITY AND DATA INTEGRITY OF BLOCKCHAIN USING DEEP LEARNING TECHNIQUES AL-KHAFAJI, Ali Khaleel Ibrahim, M.Sc., Electrical and Computer Engineering, Altınbaş University, Supervisor. Asst. Prof. Dr. Abdullahi Abdu IBRAHIM Date: 8 / 2022 Pages: 65 viii understanding of the blockchain technology and study some of the widely used blockchain frameworks including Ethereum, Bitcoin, and Litecoin. We will further examine the exclusive features offered by each of these frameworks and define their target use cases. While researching about each framework, we plan to deploy a blockchain in the local network i.e., private blockchain and operate on it from different devices running on various operating systems. In each deployment, we will observe the functional issues and benchmark system requirements for running different types of nodes. Also, we will study different algorithms involved in each framework, compare them with each other, and derive their suitability for Deep learning. Ultimately, our aim is to determine the most suitable blockchain architecture for the Deep learning ecosystem. A high-level comparison of the researched architectures will be provided so that managers and developers can quickly decide on a suitable framework for their application or use case depending upon the requirements. For each architecture, a set of sample use cases and on-going research will be discussed to get an idea of the usage of that architecture in the real world.