Both the U.S. National Institute of Standards and Technology and the Digital Benefits Hub made important announcements this morning. I will quote portions of the latter announcement.
In response to heightened fraud and related cybersecurity threats during the COVID-19 pandemic, some benefits-administering agencies began to integrate new safeguards such as individual digital accounts and identity verification, also known as identity proofing, into online applications. However, the use of certain approaches, like those reliant upon facial recognition or data brokers, has raised questions about privacy and data security, due process issues, and potential biases in systems that disproportionately impact communities of color and marginalized groups. Simultaneously, adoption of more effective, evidence-based methods of identity verification has lagged, despite recommendations from NIST (Question A4) and the Government Accountability Office.
There’s a ton to digest here. This impacts a number of issues that I and others have been discussing for years.
Normal people look forward to the latest album or movie. A biometric product marketing expert instead looks forward to an inaugural test report from the National Institute of Standards and Technology (NIST) on age estimation and verification using faces.
I’ve been waiting for this report for months now (since I initially mentioned it in July 2023), and in April NIST announced it would be available in the next few weeks.
NIST news release
Yesterday I learned of the report’s public availability via a NIST news release.
A new study from the National Institute of Standards and Technology (NIST) evaluates the performance of software that estimates a person’s age based on the physical characteristics evident in a photo of their face. Such age estimation and verification (AEV) software might be used as a gatekeeper for activities that have an age restriction, such as purchasing alcohol or accessing mature content online….
The new study is NIST’s first foray into AEV evaluation in a decade and kicks off a new, long-term effort by the agency to perform frequent, regular tests of the technology. NIST last evaluated AEV software in 2014….
(The new test) asked the algorithms to specify whether the person in the photo was over the age of 21.
Well, sort of. We’ll get to that later.
Current AEV results
I was in the middle of a client project on Thursday and didn’t have time to read the detailed report, but I did have a second to look at the current results. Like other ongoing tests, NIST will update the age estimation and verification (AEV) results as these six vendors (and others) submit new algorithms.
Why does NIST test age estmation, or anything else?
The Information Technology Laboratory and its Information Access Division
NIST campus, Gaithersburg MD. From https://www.nist.gov/ofpm/historic-preservation-nist/gaithersburg-campus. I visited it once, when Safran’s acquisition of Motorola’s biometric business was awaiting government approval. I may or may not have spoken to a Sagem Morpho employee at this meeting, even though I wasn’t supposed to in case the deal fell through.
One of NIST’s six research laboratories is its Information Technology Laboratory (ITL), charged “to cultivate trust in information technology (IT) and metrology.” Since NIST is part of the U.S. Department of Commerce, Americans (and others) who rely on information technology need an unbiased source on the accuracy and validity of this technology. NIST cultivates trust by a myriad of independent tests.
Some of those tests are carried out by one of ITL’s six divisions, the Information Access Division (IAD). This division focuses on “human action, behavior, characteristics and communication.”
The difference between FRTE and FATE
While there is a lot of IAD “characteristics” work that excites biometric folks, including ANSI/NIST standard work, contactless fingerprint capture, the Fingerprint Vendor Technology Evaluation (ugh), and other topics, we’re going to focus on our new favorite acronyms, FRTE (Face Recognition Technology Evaluation) and FATE (Face Analysis Technology Evaluation). If these acronyms are new to you, I talked about them last August (and the deprecation of the old FRVT acronym).
Basically, the difference between “recognition” and “analysis” in this context is that recognition identifies an individual, while analysis identifies a characteristic of an individual. So the infamous “Gender Shades” study, which tested the performance of three algorithms in identifying people’s sex and race, is an example of analysis.
Age analysis
The age of a person is another example of analysis. In and of itself an age cannot identify an individual, since around 385,000 people are born every day. Even with lower birth rates when YOU were born, there are tens or hundreds of thousands of people who share your birthday.
They say it’s your birthday. It’s my birthday too, yeah. From https://www.youtube.com/watch?v=fkZ9sT-z13I. Paul’s original band never filmed a promotional video for this song.
And your age matters in the situations I mentioned above. Even when marijuana is legal in your state, you can’t sell it to a four year old. And that four year old can’t (or shouldn’t) sign up for Facebook either.
You can check a person’s ID, but that takes time and only works when a person has an ID. The only IDs that a four year old has are their passport (for the few who have one) and their birth certificate (which is non-standard from county to county and thus difficult to verify). And not even all adults have IDs, especially in third world countries.
Self-testing
So companies like Yoti developed age estimation solutions that didn’t rely on government-issued identity documents. The companies tested their performance and accuracy themselves (see the PDF of Yoti’s March 2023 white paper here). However, there are two drawbacks to this:
While I am certain that Yoti wouldn’t pull any shenanigans, results from a self-test always engender doubt. Is the tester truly honest about its testing? Does it (intentionally or unintentionally) gloss over things that should be tested? After all, the purpose of a white paper is for a vendor to present facts that lead a prospect to buy a vendor’s solution.
Even with Yoti’s self tests, it did not have the ability (or the legal permission) to test the accuracy of its age estimation competitors.
How NIST tests age estimation
Enter NIST, where the scientists took a break from meterological testing or whatever to conduct an independent test. NIST asked vendors to participate in a test in which NIST personnel would run the test on NIST’s computers, using NIST’s data. This prevented the vendors from skewing the results; they handed their algorithms to NIST and waited several months for NIST to tell them how they did.
I won’t go into it here, but it’s worth noting that a NIST test is just a test, and test results may not be the same when you implement a vendor’s age estimation solution on CUSTOMER computers with CUSTOMER data.
The NIST internal report I awaited
NOW let’s turn to the actual report, NIST IR 8525 “Face Analysis Technology Evaluation: Age Estimation and Verification.”
NIST needed a set of common data to test the vendor algorithms, so it used “around eleven million photos drawn from four operational repositories: immigration visas, arrest mugshots, border crossings, and immigration office photos.” (These were provided by the U.S. Departments of Homeland Security and Justice.) All of these photos include the actual ages of the persons (although mugshots only include the year of birth, not the date of birth), and some include sex and country-of-birth information.
For each algorithm and each dataset, NIST recorded the mean absolute error (MAE), which is the mean number of years between the algorithm’s estimate age and the actual age. NIST also recorded other error measurements, and for certain tests (such as a test of whether or not a person is 17 years old) the false positive rate (FPR).
The challenge with the methodology
Many of the tests used a “Challenge-T” policy, such as “Challenge 25.” In other words, the test doesn’t estimate whether a person IS a particular age, but whether a person is WELL ABOVE a particular age. Here’s how NIST describes it:
For restricted-age applications such as alcohol purchase, a Challenge-T policy accepts people with age estimated at or above T but requires additional age assurance checks on anyone assessed to have age below T.
So if you have to be 21 to access a good or service, the algorithm doesn’t estimate if you are over 21. Instead, it estimates whether you are over 25. If the algorithm thinks you’re over 25, you’re good to go. If it thinks you’re 24, pull out your ID card.
And if you want to be more accurate, raise the challenge age from 25 to 28.
NIST admits that this procedure results in a “tradeoff between protecting young people and inconveniencing older subjects” (where “older” is someone who is above the legal age but below the challenge age).
NIST also performed a variety of demographic tests that I won’t go into here.
What the NIST age estimation test says
OK, forget about all that. Let’s dig into the results.
So depending upon the test, the best age estimation vendor (based upon accuracy and or resource usage) may be Dermalog, or Incode, or ROC (formerly Rank One Computing), or Unissey, or Yoti. Just look for that “(1)” superscript.
You read that right. Out of the 6 vendors, 5 are the best. And if you massage the data enough you can probably argue that Neurotechnology is the best also.
So if I were writing for one of these vendors, I’d argue that the vendor placed first in Subtest X, Subtest X is obviously the most important one in the entire test, and all the other ones are meaningless.
But the truth is what NIST said in its news release: there is no single standout algorithm. Different algorithms perform better based upon the sex or national origin of the people. Again, you can read the report for detailed results here.
What the report didn’t measure
NIST always clarifies what it did and didn’t test. In addition to the aforementioned caveat that this was a test environment that will differ from your operational environment, NIST provided some other comments.
The report excludes performance measured in interactive sessions, in which a person can cooperatively present and re-present to a camera. It does not measure accuracy effects related to disguises, cosmetics, or other presentation attacks. It does not address policy nor recommend AV thresholds as these differ across applications and jurisdictions.
Of course NIST is just starting this study, and could address some of these things in later studies. For example, its ongoing facial recognition accuracy tests never looked at the use case of people wearing masks until after COVID arrived and that test suddenly became important.
What about 22 year olds?
As noted above, the test used a Challenge 25 or Challenge 28 model which measured whether a person who needed to be 21 appeared to be 25 or 28 years old. This makes sense when current age estimation technology measures MAE in years, not days. NIST calculated the “inconvenience” to 21-25 (or 28) year olds affected by this method.
While a lot of attention is paid to the use cases for 21 year olds (buying booze) and 18 year olds (viewing porn), states and localities have also paid a lot of attention to the use cases for 13 year olds (signing up for social media). In fact, some legislators are less concerned about a 20 year old buying a beer than a 12 year old receiving text messages from a Meta user.
NIST tests for these in the “child online safety” tests, particularly these two:
Age < 13 – False Positive Rates (FPR) are proportions of subjects aged below 13 but whose age is estimated from 13 to 16 (below 17).
Age ≥ 17 – False Positive Rates (FPR) are proportions of subjects aged 17 or older but whose age is estimated from 13 to 16.
However, the visa database is the only one that includes data of individuals with actual ages below age 13. The youngest ages in the other datasets are 14, or 18, or even 21, rendering them useless for the child online safety tests.
Why NIST researchers are great researchers
The mark of a great researcher is their ability to continue to get funding for their research, which is why so many scientific papers conclude with the statement “further study is needed.”
Here’s how NIST stated it:
Future work: The FATE AEV evaluation remains open, so we will continue to evaluate and report on newly submitted prototypes. In future reports we will: evaluate performance of implementations that can exploit having a prior known-age reference photo of a subject (see our API); consider whether video clips afford improved accuracy over still photographs; and extend demographic and quality analyses.
Translation: if Congress doesn’t continue to give NIST money, then high school students will get drunk or high, young teens will view porn, and kids will encounter fraudsters on Facebook. It’s up to you, Congress.
Vendors and researchers are paying a lot of attention to estimating ages by using a person’s face, and all of us are awaiting NIST’s results on its age estimation tests.
But before you declare dorsal hand features as the solution to age estimation, consider:
As the title states, the study only looked at females. No idea if my masculine hand features are predictive. (Anecdotally, more males work at tasks such as bricklaying that affect the hands, including the knuckle texture that was highlighted in the study.)
As the title states, the study only looked at people from India. No idea if my American/German/English/etc. hand features are predictive. (To be fair, the subjects had a variety of skin tones.)
The study only had 1454 subjects. Better than a study that used less than 20 people, but still not enough. More research is needed.
And even with all of that, the mean absolute error in age estimation was over 4 years.
AAL1 (some confidence). AAL1, in the words of NIST, “provides some assurance.” Single-factor authentication is OK, but multi-factor authentication can be used also. All sorts of authentication methods, including knowledge-based authentication, satisfy the requirements of AAL1. In short, AAL1 isn’t exactly a “nothingburger” as I characterized IAL1, but AAL1 doesn’t provide a ton of assurance.
AAL2 (high confidence). AAL2 increases the assurance by requiring “two distinct authentication factors,” not just one. There are specific requirements regarding the authentication factors you can use. And the security must conform to the “moderate” security level, such as the moderate security level in FedRAMP. So AAL2 is satisfactory for a lot of organizations…but not all of them.
AAL3 (very high confidence). AAL3 is the highest authenticator assurance level. It “is based on proof of possession of a key through a cryptographic protocol.” Of course, two distinct authentication factors are required, including “a hardware-based authenticator and an authenticator that provides verifier impersonation resistance — the same device MAY fulfill both these requirements.”
This is of course a very high overview, and there are a lot of…um…minutiae that go into each of these definitions. If you’re interested in that further detail, please read section 4 of NIST Special Publication 800-63B for yourself.
Which authenticator assurance level should you use?
NIST has provided a handy dandy AAL decision flowchart in section 6.2 of NIST Special Publication 800-63-3, similar to the IAL decision flowchart in section 6.1 that I reproduced earlier. If you go through the flowchart, you can decide whether you need AAL1, AAL2, or the very high AAL3.
One of the key questions is the question flagged as 2, “Are you making personal data accessible?” The answer to this question in the flowchart moves you between AAL2 (if personal data is made accessible) and AAL1 (if it isn’t).
So what?
Do the different authenticator assurance levels provide any true benefits, or are they just items in a government agency’s technical check-off list?
Perhaps the better question to ask is this: what happens if the WRONG person obtains access to the data?
Could the fraudster cause financial loss to a government agency?
Threaten personal safety?
Commit civil or criminal violations?
Or, most frightening to agency heads who could be fired at any time, could the fraudster damage an agency’s reputation?
If some or all of these are true, then a high authenticator assurance level is VERY beneficial.
The Prism Project’s home page at https://www.the-prism-project.com/, illustrating the Biometric Digital Identity Prism as of March 2024. From Acuity Market Intelligence and FindBiometrics.
With over 100 firms in the biometric industry, their offerings are going to naturally differ—even if all the firms are TRYING to copy each other and offer “me too” solutions.
I’ve worked for over a dozen biometric firms as an employee or independent contractor, and I’ve analyzed over 80 biometric firms in competitive intelligence exercises, so I’m well aware of the vast implementation differences between the biometric offerings.
Some of the implementation differences provoke vehement disagreements between biometric firms regarding which choice is correct. Yes, we FIGHT.
Let’s look at three (out of many) of these implementation differences and see how they affect YOUR company’s content marketing efforts—whether you’re engaging in identity blog post writing, or some other content marketing activity.
The three biometric implementation choices
Firms that develop biometric solutions make (or should make) the following choices when implementing their solutions.
Presentation attack detection. Assuming the solution incorporates presentation attack detection (liveness detection), or a way of detecting whether the presented biometric is real or a spoof, the firm must decide whether to use active or passive liveness detection.
Age assurance. When choosing age assurance solutions that determine whether a person is old enough to access a product or service, the firm must decide whether or not age estimation is acceptable.
Biometric modality. Finally, the firm must choose which biometric modalities to support. While there are a number of modality wars involving all the biometric modalities, this post is going to limit itself to the question of whether or not voice biometrics are acceptable.
I will address each of these questions in turn, highlighting the pros and cons of each implementation choice. After that, we’ll see how this affects your firm’s content marketing.
(I)nstead of capturing a true biometric from a person, the biometric sensor is fooled into capturing a fake biometric: an artificial finger, a face with a mask on it, or a face on a video screen (rather than a face of a live person).
This tomfoolery is called a “presentation attack” (becuase you’re attacking security with a fake presentation).
And an organization called iBeta is one of the testing facilities authorized to test in accordance with the standard and to determine whether a biometric reader can detect the “liveness” of a biometric sample.
(Friends, I’m not going to get into passive liveness and active liveness. That’s best saved for another day.)
Now I could cite a firm using active liveness detection to say why it’s great, or I could cite a firm using passive liveness detection to say why it’s great. But perhaps the most balanced assessment comes from facia, which offers both types of liveness detection. How does facia define the two types of liveness detection?
Active liveness detection, as the name suggests, requires some sort of activity from the user. If a system is unable to detect liveness, it will ask the user to perform some specific actions such as nodding, blinking or any other facial movement. This allows the system to detect natural movements and separate it from a system trying to mimic a human being….
Passive liveness detection operates discreetly in the background, requiring no explicit action from the user. The system’s artificial intelligence continuously analyses facial movements, depth, texture, and other biometric indicators to detect an individual’s liveness.
Pros and cons
Briefly, the pros and cons of the two methods are as follows:
While active liveness detection offers robust protection, requires clear consent, and acts as a deterrent, it is hard to use, complex, and slow.
Passive liveness detection offers an enhanced user experience via ease of use and speed and is easier to integrate with other solutions, but it incorporates privacy concerns (passive liveness detection can be implemented without the user’s knowledge) and may not be used in high-risk situations.
So in truth the choice is up to each firm. I’ve worked with firms that used both liveness detection methods, and while I’ve spent most of my time with passive implementations, the active ones can work also.
A perfect wishy-washy statement that will get BOTH sides angry at me. (Except perhaps for companies like facia that use both.)
If you need to know a person’s age, you can ask them. Because people never lie.
Well, maybe they do. There are two better age assurance methods:
Age verification, where you obtain a person’s government-issued identity document with a confirmed birthdate, confirm that the identity document truly belongs to the person, and then simply check the date of birth on the identity document and determine whether the person is old enough to access the product or service.
Age estimation, where you don’t use a government-issued identity document and instead examine the face and estimate the person’s age.
I changed my mind on age estimation
I’ve gone back and forth on this. As I previously mentioned, my employment history includes time with a firm produces driver’s licenses for the majority of U.S. states. And back when that firm was providing my paycheck, I was financially incentivized to champion age verification based upon the driver’s licenses that my company (or occasionally some inferior company) produced.
But as age assurance applications moved into other areas such as social media use, a problem occurred since 13 year olds usually don’t have government IDs. A few of them may have passports or other government IDs, but none of them have driver’s licenses.
But does age estimation work? I’m not sure if ANYONE has posted a non-biased view, so I’ll try to do so myself.
The pros of age estimation include its applicability to all ages including young people, its protection of privacy since it requires no information about the individual identity, and its ease of use since you don’t have to dig for your physical driver’s license or your mobile driver’s license—your face is already there.
The huge con of age estimation is that it is by definition an estimate. If I show a bartender my driver’s license before buying a beer, they will know whether I am 20 years and 364 days old and ineligible to purchase alcohol, or whether I am 21 years and 0 days old and eligible. Estimates aren’t that precise.
Fingerprints, palm prints, faces, irises, and everything up to gait. (And behavioral biometrics.) There are a lot of biometric modalities out there, and one that has been around for years is the voice biometric.
I’ve discussed this topic before, and the partial title of the post (“We’ll Survive Voice Spoofing”) gives away how I feel about the matter, but I’ll present both sides of the issue.
No one can deny that voice spoofing exists and is effective, but many of the examples cited by the popular press are cases in which a HUMAN (rather than an ALGORITHM) was fooled by a deepfake voice. But voice recognition software can also be fooled.
Take a study from the University of Waterloo, summarized here, that proclaims: “Computer scientists at the University of Waterloo have discovered a method of attack that can successfully bypass voice authentication security systems with up to a 99% success rate after only six tries.”
If you re-read that sentence, you will notice that it includes the words “up to.” Those words are significant if you actually read the article.
In a recent test against Amazon Connect’s voice authentication system, they achieved a 10 per cent success rate in one four-second attack, with this rate rising to over 40 per cent in less than thirty seconds. With some of the less sophisticated voice authentication systems they targeted, they achieved a 99 per cent success rate after six attempts.
Other voice spoofing studies
Similar to Gender Shades, the University of Waterloo study does not appear to have tested hundreds of voice recognition algorithms. But there are other studies.
The 2021 NIST Speaker Recognition Evaluation (PDF here) tested results from 15 teams, but this test was not specific to spoofing.
A test that was specific to spoofing was the ASVspoof 2021 test with 54 team participants, but the ASVspoof 2021 results are only accessible in abstract form, with no detailed results.
Another test, this one with results, is the SASV2022 challenge, with 23 valid submissions. Here are the top 10 performers and their error rates.
You’ll note that the top performers don’t have error rates anywhere near the University of Waterloo’s 99 percent.
So some firms will argue that voice recognition can be spoofed and thus cannot be trusted, while other firms will argue that the best voice recognition algorithms are rarely fooled.
What does this mean for your company?
Obviously, different firms are going to respond to the three questions above in different ways.
For example, a firm that offers face biometrics but not voice biometrics will convey how voice is not a secure modality due to the ease of spoofing. “Do you want to lose tens of millions of dollars?”
A firm that offers voice biometrics but not face biometrics will emphasize its spoof detection capabilities (and cast shade on face spoofing). “We tested our algorithm against that voice fake that was in the news, and we detected the voice as a deepfake!”
There is no universal truth here, and the message your firm conveys depends upon your firm’s unique characteristics.
And those characteristics can change.
Once when I was working for a client, this firm had made a particular choice with one of these three questions. Therefore, when I was writing for the client, I wrote in a way that argued the client’s position.
After I stopped working for this particular client, the client’s position changed and the firm adopted the opposite view of the question.
Therefore I had to message the client and say, “Hey, remember that piece I wrote for you that said this? Well, you’d better edit it, now that you’ve changed your mind on the question…”
Bear this in mind as you create your blog, white paper, case study, or other identity/biometric content, or have someone like the biometric content marketing expert Bredemarket work with you to create your content. There are people who sincerely hold the opposite belief of your firm…but your firm needs to argue that those people are, um, misinformed.
As further proof that I am celebrating, rather than hiding, my “seasoned” experience—and you know what the code word “seasoned” means—I am entitling this blog post “Take Me to the Pilot.”
Although I’m thinking about a different type of “pilot”—a pilot to establish that Login.gov can satisfy Identity Assurance Level 2 (IAL2).
The link in that sentence directs the kind reader to a post I wrote in November 2023, detailing that fact that the GSA Inspector General criticized…the GSA…for implying that Login.gov was IAL2-compliant when it was not. The November post references a GSA-authored August blog post which reads in part (in bold):
Login.gov is on a path to providing an IAL2-compliant identity verification service to its customers in a responsible, equitable way.
Specifically, over the next few months, Login.gov will:
Pilot facial matching technology consistent with the National Institute of Standards and Technology’s Digital Identity Guidelines (800-63-3) to achieve evidence-based remote identity verification at the IAL2 level….
Using proven facial matching technology, Login.gov’s pilot will allow users to match a live selfie with the photo on a self-supplied form of photo ID, such as a driver’s license. Login.gov will not allow these images to be used for any purpose other than verifying identity, an approach which reflects Login.gov’s longstanding commitment to ensuring the privacy of its users. This pilot is slated to start in May with a handful of existing agency-partners who have expressed interest, with the pilot expanding to additional partners over the summer. GSA will simultaneously seek an independent third party assessment (Kantara) of IAL2 compliance, which GSA expects will be completed later this year.
In short, GSA’s April 11 press release about the Login.gov pilot says that it expects to complete IAL2 compliance later this year. So it’s going to take more than a year for the GSA to repair the gap that its Inspector General identified.
My seasoned response
Once I saw Steve’s update this morning, I felt it sufficiently important to share the news among Bredemarket’s various social channels.
With a picture.
B-side of Elton John “Your Song” single issued 1970.
For those of you who are not as “seasoned” as I am, the picture depicts the B-side of a 1970 vinyl 7″ single (not a compact disc) from Elton John, taken from the album that broke Elton in the United States. (Not literally; that would come a few years later.)
By the way, while the original orchestrated studio version is great, the November 1970 live version with just the Elton John – Dee Murray – Nigel Olsson trio is OUTSTANDING.
Back to Bredemarket social media. If you go to my Instagram post on this topic, I was able to incorporate an audio snippet from “Take Me to the Pilot” (studio version) into the post. (You may have to go to the Instagram post to actually hear the audio.)
Not that the song has anything to do with identity verification using government ID documents paired with facial recognition. Or maybe it does; Elton John doesn’t know what the song means, and even lyricist Bernie Taupin doesn’t know what the song means.
So from now on I’m going to say that “Take Me to the Pilot” documents future efforts toward IAL2 compliance. Although frankly the lyrics sound like they describe a successful iris spoofing attempt.
Through a glass eye, your throne Is the one danger zone
The Georgia bill explicitly mentions Identity Assurance Level 2.
Under the bill, the age verification methods would have to meet or exceed the National Institute of Standards and Technology’s Identity Assurance Level 2 standard.
So if you think you can use Login.gov to access a porn website, think again.
There’s also a mention of mobile driver’s licenses, albeit without a corresponding mention of the ISO/IEC 18013-5:2021.
Specifically mentioned in the bill text is “digitized identification cards,” described as “a data file available on a mobile device with connectivity to the internet that contains all of the data elements visible on the face and back of a driver’s license or identification card.”
So digital identity is becoming more important for online access, as long as certain standards are met.
Before you can fully understand the difference between personally identifiable information (PII) and protected health information (PHI), you need to understand the difference between biometrics and…biometrics. (You know sometimes words have two meanings.)
Designed by Google Gemini.
The definitions of biometrics
To address the difference between biometrics and biometrics, I’ll refer to something I wrote over two years ago, in late 2021. In that post, I quoted two paragraphs from the International Biometric Society that illustrated the difference.
Since the IBS has altered these paragraphs in the intervening years, I will quote from the latest version.
The terms “Biometrics” and “Biometry” have been used since early in the 20th century to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences.
Statistical methods for the analysis of data from agricultural field experiments to compare the yields of different varieties of wheat, for the analysis of data from human clinical trials evaluating the relative effectiveness of competing therapies for disease, or for the analysis of data from environmental studies on the effects of air or water pollution on the appearance of human disease in a region or country are all examples of problems that would fall under the umbrella of “Biometrics” as the term has been historically used….
The term “Biometrics” has also been used to refer to the field of technology devoted to the identification of individuals using biological traits, such as those based on retinal or iris scanning, fingerprints, or face recognition. Neither the journal “Biometrics” nor the International Biometric Society is engaged in research, marketing, or reporting related to this technology. Likewise, the editors and staff of the journal are not knowledgeable in this area.
In brief, what I call “broad biometrics” refers to analyzing biological sciences data, ranging from crop yields to heart rates. Contrast this with what I call “narrow biometrics,” which (usually) refers only to human beings, and only to those characteristics that identify human beings, such as the ridges on a fingerprint.
The definition of “personally identifiable information” (PII)
Information that can be used to distinguish or trace an individual’s identity, either alone or when combined with other information that is linked or linkable to a specific individual.
Note the key words “alone or when combined.” The ten numbers “909 867 5309” are not sufficient to identify an individual alone, but can identify someone when combined with information from another source, such as a telephone book.
What types of information can be combined to identify a person? The U.S. Department of Defense’s Privacy, Civil Liberties, and Freedom of Information Directorate provides multifarious examples of PII, including:
Social Security Number.
Passport number.
Driver’s license number.
Taxpayer identification number.
Patient identification number.
Financial account number.
Credit card number.
Personal address.
Personal telephone number.
Photographic image of a face.
X-rays.
Fingerprints.
Retina scan.
Voice signature.
Facial geometry.
Date of birth.
Place of birth.
Race.
Religion.
Geographical indicators.
Employment information.
Medical information.
Education information.
Financial information.
Now you may ask yourself, “How can I identify someone by a non-unique birthdate? A lot of people were born on the same day!”
But the combination of information is powerful, as researchers discovered in a 2015 study cited by the New York Times.
In the study, titled “Unique in the Shopping Mall: On the Reidentifiability of Credit Card Metadata,” a group of data scientists analyzed credit card transactions made by 1.1 million people in 10,000 stores over a three-month period. The data set contained details including the date of each transaction, amount charged and name of the store.
Although the information had been “anonymized” by removing personal details like names and account numbers, the uniqueness of people’s behavior made it easy to single them out.
In fact, knowing just four random pieces of information was enough to reidentify 90 percent of the shoppers as unique individuals and to uncover their records, researchers calculated. And that uniqueness of behavior — or “unicity,” as the researchers termed it — combined with publicly available information, like Instagram or Twitter posts, could make it possible to reidentify people’s records by name.
Now biometrics only form part of the multifarious list of data cited above, but clearly biometric data can be combined with other data to identify someone. An easy example is taking security camera footage of the face of a person walking into a store, and combining that data with the same face taken from a database of driver’s license holders. In some jurisdictions, some entities are legally permitted to combine this data, while others are legally prohibited from doing so. (A few do it anyway. But I digress.)
Because narrow biometric data used for identification, such as fingerprint ridges, can be combined with other data to personally identify an individual, organizations that process biometric data must undertake strict safeguards to protect that data. If personally identifiable information (PII) is not adequately guarded, people could be subject to fraud and other harms.
The definition of “protected health information” (PHI)
Protected Health Information. The Privacy Rule protects all “individually identifiable health information” held or transmitted by a covered entity or its business associate, in any form or media, whether electronic, paper, or oral. The Privacy Rule calls this information “protected health information (PHI).”12
“Individually identifiable health information” is information, including demographic data, that relates to:
the individual’s past, present or future physical or mental health or condition,
the provision of health care to the individual, or
the past, present, or future payment for the provision of health care to the individual,
and that identifies the individual or for which there is a reasonable basis to believe it can be used to identify the individual.13 Individually identifiable health information includes many common identifiers (e.g., name, address, birth date, Social Security Number).
The Privacy Rule excludes from protected health information employment records that a covered entity maintains in its capacity as an employer and education and certain other records subject to, or defined in, the Family Educational Rights and Privacy Act, 20 U.S.C. §1232g.
Now there’s obviously an overlap between personally identifiable information (PII) and protected health information (PHI). For example, names, dates of birth, and Social Security Numbers fall into both categories. But I want to highlight two things are are explicitly mentioned as PHI that aren’t usually cited as PII.
Physical or mental health data. This could include information that a medical professional captures from a patient, including biometric (broad biometric) information such as heart rate or blood pressure.
Health care provided to an individual. This not only includes written information such as prescriptions, but oral information (“take two aspirin and call my chatbot in the morning”). Yes, chatbot. Deal with it. Dr. Marcus Welby and his staff retired a long time ago.
Robert Young (“Marcus Welby”) and Jane Wyatt (“Margaret Anderson” on a different show). By ABC TelevisionUploaded by We hope at en.wikipedia – eBay itemphoto informationTransferred from en.wikipedia by SreeBot, Public Domain, https://commons.wikimedia.org/w/index.php?curid=16472486
Because broad biometric data used for analysis, such as heart rates, can be combined with other data to personally identify an individual, organizations that process biometric data must undertake strict safeguards to protect that data. If protected health information (PHI) is not adequately guarded, people could be subject to fraud and other harms.
Simple, isn’t it?
Actually, the parallels between identity/biometrics and healthcare have fascinated me for decades, since the dedicated hardware to capture identity/biometric data is often similar to the dedicated hardware to capture health data. And now that we’re moving away from dedicated hardware to multi-purpose hardware such as smartphones, the parallels are even more fascinating.
Some firms make claims and don’t support them, while others support their claims with quantified benefits. But does quantifying help or harm the firms that do it? This pudding post answers this question…and then twists toward the identity/biometrics market at the end.
The “me too” players in the GCP market
Whoops.
In that heading above, I made a huge mistake by introducing an acronym without explaining it. So I’d better correct my error.
GCP stands for Glowing Carbonated Pudding.
I can’t assume that you already knew this acronym, because I just made it up. But I can assure you that the GCP market is a huge market…at least in my brain. All the non-existent kids love the scientifically advanced and maximally cool pudding that glows in the dark and has tiny bubbles in it.
Glowing Carbonated Pudding. Designed by Google Bard. Yeah, Google Bard creates images now.
Now if you had studied this non-existent market like I have, you’ll realize from the outset that most of the players don’t really differentiate their offerings. Here are a few examples of firms with poor product marketing:
Jane Spain GCP: “Trust us to provide good GCP.”
Betty Brazil GCP: “Trust us to provide really good GCP.”
Clara Canada GCP: “Trust us to provide great GCP.”
You can probably figure out what happened here.
The CEO at Betty Brazil told the company’s product marketers, “Do what Jane Spain did but do it better.”
After that Clara Canada’s CEO commanded, “Do what Betty Brazil did but do it better.” (I’ll let you in on a little secret. Clara Canada’s original slogan refereneced “the best GCP,” but Legal shot that down.)
But another company, Wendy Wyoming, decided to differentiate itself, and cited independent research as its differentiator.
Wendy Wyoming Out of This World GCP satisfies you, and we have independent evidence to prove it!
The U.S. National Institute of Standards and Technology, as part of its Pudding User Made (PUM, not FRTE) Test, confirmed that 80% of all Wendy Wyoming Out of This World GCP mixes result in pudding that both glows and is carbonated. (Mix WW3, submitted November 30, 2023; not omnigarde-003)
Treat your child to science-backed cuisine with Wendy Wyoming Out of This World GCP!Wendy Wyoming is a top tier (excluding Chinese mixes) GCP provider.
But there are other competitors…
The indirect competitor who questions the quantified benefits
There are direct competitors that provide the same product as Wendy Wyoming, Jane Spain, and everyone else.
And then there are indirect competitors who provide non-GCP alternatives that can substitute for GCPs.
For example, Polly Pennsylvania is NOT a GCP provider. It makes what the industry calls a POPS, or a Plain Old Pudding Sustenance. Polly Pennsylvania questions everything about GCP…and uses Wendy Wyoming’s own statistics against it.
Designed by Google Bard.
Fancy technologies have failed us.
If you think that one of these GCP puddings will make your family happy, think again. A leading GCP provider has publicly admitted that 1 out of every 5 children who buy a GCP won’t get a GCP. Either it won’t glow, or it’s not carbonated. Do you want to make your kid cry?
Treat your child to the same pudding that has satisfied many generations. Treat your child to Polly Pennsylvania Perfect POPS.
Pennsylvania Perfect remembers.
So who wins?
It looks like Polly Pennsylvania and Wendy Wyoming have a nasty fight on their hands. One that neck-deep marketers like to call a “war.” Except that nobody dies. (Sadly, that’s not true.)
Some people think that Wendy Wyoming wins because 4 out of 5 of their customers receive true GCP.
Others think that Polly Pennsylvaia wins because 5 out of 5 of their customers get POPS pudding.
But it’s clear who lost.
All the Jane Spains and Betty Brazils who didn’t bother to create a distinctive message.
Don’t be Jane Spain. Explain why your product is the best and all the other products aren’t.
Copying the competition doesn’t differentiate you. Trust me.
The “hungry people” (target audience) for THIS post
Oh, and if you didn’t figure it out already, this post was NOT intended for scientific pudding manufacturers. It was intended for identity/biometric firms who can use some marketing and writing help. Hence the references to NIST and the overused word “trust.”
If you’re hungry to kickstart your identity/biometric firm’s written content, click on the image below to learn about Bredemarket’s services.
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