
Explore the privacy challenges in Web3 and how homomorphic encryption enables a privacy layer for DeFi and other sensitive data applications.
Identify who benefits from homomorphic encryption, targeting managers, leaders, and entrepreneurs, and explore the topic at a broader level while offering reading material for deeper mathematics and cryptography interest.
Introduce cryptography and homomorphic encryption, covering partial and fully homomorphic schemes, privacy-preserving data processing, outsourced computations, and standardization efforts, with Web3 and machine learning applications.
Explore the basics of cryptography, the science of secure communication, covering encryption and decryption, symmetric and asymmetric cryptography, keys and algorithms, and core goals of confidentiality, integrity, authenticity, and non-repudiation.
Trace the development of homomorphic encryption from 1978 origins to Gentry's 2009 fully homomorphic breakthrough. Highlight key players and open-source libraries enabling secure data analysis and privacy preserving machine learning.
Explore how homomorphic encryption lets operations occur on ciphertexts without decrypting, focusing on homomorphic addition that yields the plaintext sum. Applications include secure voting and privacy preserving data aggregation.
Explore partially homomorphic encryption, which supports addition or multiplication on encrypted data without decryption. Learn how Paillier enables secure data aggregation and Goldwasser-Micali enables secure multiplications.
Learn how bootstrapping reduces noise in fully homomorphic encryption by decrypting and re-encrypting ciphertexts, enabling secure, repeated computations despite computational costs.
Encrypt data with homomorphic encryption to perform computations on ciphertext while preserving privacy. Decrypt results to reveal plaintext-equivalent outcomes for secure cloud computing, healthcare analytics, and encrypted data sharing.
Outsourced computation with homomorphic encryption lets providers run computations on encrypted data and return encrypted results for the data owner to decrypt, enabling secure data analytics and privacy-preserving machine learning.
Explore homomorphic encryption schemes such as RSA, Paillier, BFV, and CKKS, and gain hands-on with tools like Microsoft SEAL, Stencil, Palisade, HElib, SEAL-SIX, and TF Encrypted.
National Institute of Standards and Technology leads the standardization efforts for fully homomorphic encryption through the homomorphic encryption standardization project and the homomorphic encryption.org consortium, engaging industry, government, and academia.
Welcome to the "Basics of Homomorphic Encryption" course.
In today's interconnected world, where data is the lifeblood of innovation, protecting sensitive information while still enabling powerful computations has become a paramount challenge. Homomorphic Encryption emerges as a groundbreaking solution to this challenge, opening new possibilities for privacy-preserving data processing and secure outsourcing of computations.
Homomorphic Encryption is a very complex topic involving complex mathematics and technical concepts. This course tries to explain this complex subject in simple language for a broader audience, especially for business-facing leaders.
Apart from discussing the basics of cryptography and Homomorphic Encryption, the course delves primarily into the use cases. We first discuss the basic use cases - Privacy-Preserving Data Processing and Outsourced Computation.
Later we delve into the hybrid use cases - Privacy Preserving Smart Contracts, Private On-Chain Transactions, use of Homomorphic Encryption in SMPC, and use of Homomorphic Encryption in Machine Learning.
Now, this course primarily deals with what kind of problems Homomorphic Encryption is and can solve. If you are looking for a course that can help you implement Homomorphic Encryption with coding examples, this is probably not a good match.
If you are more theoretically inclined, please refer to the papers attached to the lectures.
All the best.