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Real World Cryptography with TLS 1.3 and Rust

It took four years of collaboration among cryptographers, engineers, and computer scientists for the IETF to finalize the TLS 1.3 specification (RFC 8446). This major revision eliminated many legacy cryptographic features from TLS 1.2, simplifying the protocol and strengthening its security. Since its release, TLS 1.3 has been adopted by over 80% of websites and is now widely used by numerous application-level protocols, including secure messaging systems and client-server architectures.

At the heart of TLS 1.3 lies a small set of well-understood cryptographic primitives and sub-protocols. These are foundational tools used across many secure systems. Understanding how they work—and how to use them correctly—is essential for anyone building modern, secure applications.

Rust has emerged as a leading language for secure system development, thanks to its strong safety guarantees and innovative features. In this course, we will use Rust to implement a full-featured TLS 1.3 client. Along the way, we’ll explore key elements of Rust’s security model, including the borrow checker, immutability, traits, structured composition, and robust error handling.

Whether you’re interested in cryptographic protocols or systems programming, this course offers a hands-on introduction to both—with real-world relevance and modern tools.

Dates: 2nd and 3rd week of July 2025 (Two credit program)

Learning Outcomes

On completion, students will be able to:

Course Contents

Fee Details

The Program fee is USD 955 (Appx).

Included in the Fee:

Not Included in the Fee:

Pre-requisites

  • Essential linear algebra for deep learning
  • Fundamentals of linear classification: weights, bias, scores, and loss functions
  • Calculus for the gradient descent algorithm
  • Forward and backward propagation with regularization
  • Batch processing for large datasets
  • Linear to nonlinear classification via activation functions
  • Computational setup of a shallow neural network
  • Tuning neural network performance
  • Pre-processing data and batch normalization
  • Cross-validation for validating model performance
  • Extending the computational setup from a shallow to a deep neural network
  • Introduction to the TensorFlow library
  • Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.

Name of the Coordinators

Dr. Devi Prasad M
Dr. Shaila Angela Lewis
Ms. Priya Kamath

Coordinator Details

Program Highlights

Offered by

Manipal School of Information Sciences.