Distributed Ledger Technologies

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Distributed Ledger Technologies

This presentation introduces Distributed Ledger Technologies (DLTs), explaining their core principles, cryptographic foundations, and consensus mechanisms. It covers how transactions are created, validated, and recorded, compares major consensus approaches, and discusses scalability and sustainability challenges. A practical Python-based DLT simulation is presented to illustrate how these concepts are implemented in real systems.

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What you will learn

  • ledger
  • blockchain
  • bitcoin
  • decentralization
  • smartcontracts

Courselet Content

2 components

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General Overview

Description

This presentation provides a technical overview of Distributed Ledger Technologies (DLTs) as replicated state machines operating over decentralized networks. It introduces the fundamental components of DLTs, including nodes, transactions, cryptographic primitives (hash functions and digital signatures), and data structures such as blocks and hash-linked ledgers. Multiple consensus paradigms are analyzed, including leader-based, voting-based, and graph-based approaches, with particular attention to their security assumptions, performance trade-offs, and finality properties.
The presentation further examines practical limitations related to scalability, energy efficiency, and network synchronization, and surveys common solutions such as sharding, layer-2 protocols, and alternative ledger architectures. To ground the theoretical discussion, a Python-based DLT simulation is presented, demonstrating transaction creation, validation, replay protection, majority-vote consensus, block formation, and state consistency across nodes. The goal is to provide both conceptual understanding and implementation-level insight into how modern distributed ledgers function and why their design choices matter.

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Meet the instructors !

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About the Instructor

Blockchains, Crypto Currencies, Network Analytics (WS 25/26) class

instructor
About the Instructor

Blockchains, Crypto Currencies, Network Analytics (WS 25/26) class