Author Archives: bredemarket

Marketing and writing services.

Now We’re Talking About Emojis

Summer Christine Duffield of New York has filed a lawsuit against the Walt Disney Company and related entities regarding alleged privacy violations at Disney’s California theme parks. I’m not sure whether she’s basing it on California law or something else, because the only cited cause of action in the summary of the filing is 28 U.S.C. § 1332 oc Diversity-Other Contract, which basically says that federal courts can handle civil cases over $75,000. Duffield is asking for millions.

At the Disney parks, some entrance lanes use facial recognition. Others do not.

In Biometric Update’s summary, it sounds like an EMOJI is at issue.

“The confusion here is in an emoji or graphic that Disney has used to indicate lanes for which facial recognition will not be used. The graphic shows a face with a slash through it – like no smoking, but for biometric data collection. However, the distinction is minor enough to cause confusion on its own, and Disney has not helped its case by failing to put, on its privacy notice, a working emoji; as it stands, customers may be forgiven for not understanding the difference between the two lanes, which Disney suggests are labeled identically.”

So much for emojis being straightforward. They can be interpreted in many ways. In the picture with this post, Google Gemini thought that the Disney symbol meant something entirely different.

Non-Human Identity Verification

How do you verify non-human identities?

One of the reasons that I titled my ebook “Proving Humanity” is because the six (yes, six) factors of identity verification and authentication that I discuss only apply to identifying humans, and do not apply to non-human identities.

Again, so how do you verify non-human identities?

Cryptographics

One way is via cryptographics. As I discussed previously, the Secure Production Identity Framework For Everyone (SPIFFE) and the SPIFFE Runtime Environment (SPIRE) provide non-person entities with “strongly attested, cryptographic identities.”

Problem solved, right?

As any human who has used a password knows, a single factor can be stolen. And that includes cryptographic factors.

Provenance

Which means that we have to look at provenance. But instead of looking at the provenance of an AI-generated image or video, we are looking at the provenance of an agent that performs actions. The network origin. The environment. The associated attributes. Is the agent running on a specific, authorized, and known virtual machine or container at a specific network address, or is it running…somewhere else?

Behavior

And if you’ve read my book, you know that human identities can be evaluated based upon their behavior (either tendencies or intent). You can also look at the behavior of agents. Is the agent acting at an unexpected time of day? Is it executing an unusually high volume of requests? Is it “scoping out the joint”?

Multi-factor authentication

Again, it’s possible to spoof one factor, but much harder to spoof multiple factors. And that applies to both humans and non-human agents.

Be safe out there.

How Do You Talk About the Product “Plumbing”?

There are a variety of hungry people (target audiences) who look at your product marketing content. And they all have different needs.

  • When talking about an elegant water fountain, some readers only care that the fountain works.
  • Other readers want to know HOW it works. Issues such as support and maintenance are critically important to these folks, but matter little to the first group who simply wants a working fountain.

If you are forced to speak to both target audiences in a single piece of content, how do you do it?

Very carefully.

My preference is to discuss the high-level benefits at the beginning of the content, and save the more technical uptime details and/or feature lists for later in the narrative.

Unless you are ONLY speaking to technical folks, leading with the “plumbing” kills your content. Someone who wants their police agency to solve more burglaries will fall asleep at a mention of 1000 pixels per inch fingerprint resolution or NIST-compliant lower palm print image dimensions.

Stay light, and only go deep to buttress your lightness.

Is It Harder to Monitor Confidential Data Transfers?

In the pre-digital days, if you wanted to transfer confidential data you had to hand-carry it.

Now it is possible to track movements of confidential data digitally.

If data moves off a laptop you can track it.

Google Gemini.

Unless it moves off a laptop or a smartphone that you’re NOT monitoring.

Oops.

Data Centers: NIMBY? Part Two.

We want bad people to be thrown in prison, but we don’t want said prisons near OUR houses. Same for data centers, in West Virginia and elsewhere.

I first heard of Festus, Missouri via one of those long-winded Facebook posts that doesn’t cite its sources, thus making me automatically question its veracity.

But this one was true, according to Politico.

“The [Festus] City Council voted March 30 to approve a development agreement for the data center, planned for 360 wooded acres on the city’s southwest side. The operator of the data center hasn’t been identified…”

Now normally there are weeks of meetings before a city council even approves a fast food joint. This leveling of 360 acres of wood to let people like me create wildebeest pictures seems to have surprised the residents of Festus.

Google Gemini. Yes, I appreciate the irony.

But that wasn’t the only surprise for the city. A second surprise happened a few days later.

“Voters in a small Missouri town, unhappy with the city council’s approval of a $6 billion data center, struck back at the polls last week, ousting all four incumbent council members running for reelection.”

If you are a political (or business) leader who despises transparency, try not to violate your stakeholders’ trust when your job is on the line.

Speaking of losing jobs, there is an effort to recall Mayor Sam Richards and other council members who supported the data center project.

Can Someone With No Biometric Knowledge Write Your Biometric Product Content?

Sure! But…

…they will need a lot of guidance and editing from you.

Biometric product marketing expert.

Get your product content right the first time with Bredemarket, the biometric product marketing expert.

Click below.

Stop losing prospects! Use Bredemarket content for tech marketers

If You Can’t Make It, Fake It: Generation of Synthetic Faces for Algorithmic Testing

Sometimes it seems like there’s a catch-22 in facial algorithm development. On the one hand, opponents complain: “How do you know these algorithms work if they’ve never been tested on real faces?” Then in the next breath they complain, “You can’t use the faces of real people to test your algorithms! That violates their privacy!”

So what do you do?

Fake it.

There are many ways to create fake faces for enterprise and consumer use, but how do we know that synthetic faces are sufficiently representative of real ones?

That’s the challenges these researchers faced:

“Face recognition models are trained on large-scale datasets, which have privacy and ethical concerns. Lately, the use of synthetic data to complement or replace genuine data for the training of face recognition models has been proposed. While promising results have been obtained, it still remains unclear if generative models can yield diverse enough data for such tasks. In this work, we introduce a new method, inspired by the physical motion of soft particles subjected to stochastic Brownian forces, allowing us to sample identities distributions in a latent space under various constraints. We introduce three complementary algorithms, called Langevin, Dispersion, and DisCo, aimed at generating large synthetic face datasets. With this in hands, we generate several face datasets and benchmark them by training face recognition models, showing that data generated with our method exceeds the performance of previously GAN-based datasets and achieves competitive performance with state-of-the-art diffusion-based synthetic datasets. While diffusion models are shown to memorize training data, we prevent leakage in our new synthetic datasets, paving the way for more responsible synthetic datasets.”

If you want to see the synthetic data these researchers created, and if you have the ability to uncompress tar.gz files (Mac and Windows 11 support this), visit this page.