Lecture

Cryptographic Hash Functions and Block Chains

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In course

COM-401: Cryptography and security

This course introduces the basics of cryptography. We review several types of cryptographic primitives, when it is safe to use them and how to select the appropriate security parameters. We detail how they work and sketch how they can be implemented. ...

Description

This lecture covers cryptographic hash functions, message authentication codes, and case studies on block chains. It explains the concepts of sponge functions, one round of f, and hash functions like MD5, SHA1, SHA2, and SHA3. The case study on block chains delves into bitcoins, bitcoin transactions, proof-of-work, and the structure of a block chain. Additionally, it discusses MAC, MAC primitives, security aspects, HMAC, examples of hashing, and different MAC variants like CMAC, PMAC, and WC-MAC. The lecture also explores universal hash functions, WC-MAC using stream ciphers, and authenticated modes of operation like CCM and GCM mode.

Instructor

Serge Vaudenay

Serge Vaudenay entered at the Ecole Normale Supérieure in 1989 with a major in mathematics. He earned his agrégation (secondary teaching degree) in mathematics in 1992, then a PhD in Computer Science at the University of Paris 7 - Denis Diderot in 1995. He subsequently became a senior research fellow at the CNRS, prior to being granted his habilitation à diriger des recherches (a postdoctoral degree authorizing the recipient to supervise doctoral students). In 1999, he was appointed as a Professor at the EPFL, where he created the Security and Cryptography Laboratory.

Official source

Ontological neighbourhood

Computer engineering

Computer security: Public-key cryptography

Related concepts (32)

Cryptographic hash function

A cryptographic hash function (CHF) is a hash algorithm (a map of an arbitrary binary string to a binary string with a fixed size of bits) that has special properties desirable for a cryptographic application: the probability of a particular -bit output result (hash value) for a random input string ("message") is (as for any good hash), so the hash value can be used as a representative of the message; finding an input string that matches a given hash value (a pre-image) is unfeasible, assuming all input str

Hash function

A hash function is any function that can be used to map data of arbitrary size to fixed-size values, though there are some hash functions that support variable length output. The values returned by a hash function are called hash values, hash codes, digests, or simply hashes. The values are usually used to index a fixed-size table called a hash table. Use of a hash function to index a hash table is called hashing or scatter storage addressing.

Mutual authentication

Mutual authentication or two-way authentication (not to be confused with two-factor authentication) refers to two parties authenticating each other at the same time in an authentication protocol. It is a default mode of authentication in some protocols (IKE, SSH) and optional in others (TLS). Mutual authentication is a desired characteristic in verification schemes that transmit sensitive data, in order to ensure data security. Mutual authentication can be accomplished with two types of credentials: usernames and passwords, and public key certificates.

HMAC

In cryptography, an HMAC (sometimes expanded as either keyed-hash message authentication code or hash-based message authentication code) is a specific type of message authentication code (MAC) involving a cryptographic hash function and a secret cryptographic key. As with any MAC, it may be used to simultaneously verify both the data integrity and authenticity of a message. HMAC can provide authentication using a shared secret instead of using digital signatures with asymmetric cryptography.

Universal hashing

In mathematics and computing, universal hashing (in a randomized algorithm or data structure) refers to selecting a hash function at random from a family of hash functions with a certain mathematical property (see definition below). This guarantees a low number of collisions in expectation, even if the data is chosen by an adversary. Many universal families are known (for hashing integers, vectors, strings), and their evaluation is often very efficient.

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