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Lecture

Authentication - Biometrics

In course

COM-301: Computer security and privacy

This is an introductory course to computer security and privacy. Its goal is to provide students with means to reason about security and privacy problems, and provide them with tools to confront them. ...

Description

This lecture covers the problems with passwords, the concept of biometrics, including fingerprint and face recognition, the process of enrolling and verifying biometrics, and the importance of balancing false positives and false negatives in biometric authentication.

Instructors (2)

Carmela González Troncoso

Edouard Bugnion

Edouard Bugnion joined EPFL in 2012, where his focus is on datacenter systems. His areas of interest include operating systems, datacenter infrastructure (systems and networking), and computer architecture. Before joining EPFL, Edouard spent 18 years in the US, where he studied at Stanford and co-founded two startups: VMware and Nuova Systems (acquired by Cisco). At VMware from 1998 until 2005, he played many roles including CTO. At Nuova/Cisco from 2005 until 2011, he helped build the core engineering team and became the VP/CTO of Ciscos Server, Access, and Virtualization Technology Group, a group that brought to market Ciscos Unified Computing System (UCS) platform for virtualized datacenters. Prof. Bugnion is a Fellow of the ACM. Together with his colleagues, he received the ACM Software System Award for VMware 1.0 in 2009. His paper Disco: Running Commodity Operating Systems on Scalable Multiprocessors" received a Best Paper Award at SOSP '97 and was entered into the ACM SIGOPS Hall of Fame Award in 2008. At EPFL, he received the OSDI 2014 Best Paper Award for his work on the IX dataplane operating system

Official source

Related concepts (29)

Biometrics

Biometrics are body measurements and calculations related to human characteristics. Biometric authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Biometric identifiers are often categorized as physiological characteristics which are related to the shape of the body.

False positives and false negatives

A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a and a ).

Password

A password, sometimes called a passcode (for example in Apple devices), is secret data, typically a string of characters, usually used to confirm a user's identity. Traditionally, passwords were expected to be memorized, but the large number of password-protected services that a typical individual accesses can make memorization of unique passwords for each service impractical. Using the terminology of the NIST Digital Identity Guidelines, the secret is held by a party called the claimant while the party verifying the identity of the claimant is called the verifier.

Multi-factor authentication

Multi-factor authentication (MFA; two-factor authentication, or 2FA, along with similar terms) is an electronic authentication method in which a user is granted access to a website or application only after successfully presenting two or more pieces of evidence (or factors) to an authentication mechanism. MFA protects personal data—which may include personal identification or financial assets—from being accessed by an unauthorized third party that may have been able to discover, for example, a single password.

False positive rate

In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm ratio) is the probability of falsely rejecting the null hypothesis for a particular test. The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.

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