Privacy-enhancing technologies in practice: the hype is ahead of the implementation

0:00Welcome to the Leaders Insights podcast.

0:07Today's episode, Privacy Enhancing Technologies in Practice.

0:11The hype is ahead of the implementation.

0:14Everyone's boardroom is buzzing about privacy enhancing technologies.

0:18Tools that let you compute on data without exposing it.

0:22You've deployed them.

0:23Tell me the uncomfortable part.

0:25The uncomfortable part is that most of what gets called a deployment is a demo that

0:29survived a friendly audience.

0:32The science is genuinely sound.

0:34The production reality is that the gap between a slide and a running system is measured in

0:39years and headcount you don't have.

0:42That sounds like the usual enterprise is slow excuse.

0:45Be specific.

0:46Take fully homomorphic encryption, computing on encrypted data without ever decrypting it.

0:52Beautiful math.

0:54In practice, it runs thousands to millions of times slower than plain computation.

0:59IBM and Zama have made real strides.

1:02But when a vendor tells you it's production ready, ask them to run your fraud model on

1:06it and report the latency.

1:08I've watched a query that took 40 milliseconds balloon to 15 minutes.

1:12So the technology just doesn't work?

1:15It works.

1:16It's just priced wrong for most jobs.

1:18That's the thing nobody says out loud.

1:20You deploy encrypted computation to keep a medical genome private.

1:24Worth every second.

1:26You would not deploy it to check whether someone clicked an ad.

1:29What about the safer sounding ones?

1:31Differential privacy, adding statistical noise so no single person can be identified.

1:36The US Census used it.

1:38They did in 2020.

1:40And it's the best cautionary tale we have.

1:43The census added mathematical noise to protect individuals.

1:46And demographers screamed because small town population counts came out visibly wrong.

1:51That's the trade nobody frames honestly.

1:54Privacy and accuracy are a dial, not a switch.

1:57Open up protection.

1:59Your numbers get blurrier.

2:00Every vendor pitch skips the part where your analysts lose precision they were relying

2:04on.

2:05You're a chief data officer.

2:07Aren't you supposed to be selling this internally?

2:09I'm supposed to not get fired for a breach or a lawsuit, which is a different job.

2:14And I'll tell you the dirty secret.

2:16Half the privacy problems teams are trying to solve with exotic cryptography can be solved

2:20by deleting data they never needed to collect.

2:23Radical, I know.

2:25That's the funny because it's true line.

2:27But give me a case where the fancy stuff actually paid off.

2:31Synthetic data, artificial records generated to mimic real ones statistically without

2:36being real people.

2:38American Express and several banks use it to test systems without touching live customer

2:43data.

2:44That's a genuine win because the accuracy bar for does my software crash is low.

2:51Where it fails is when someone trains a credit model on synthetic data and acts surprised

2:56when it misses the rare cases that actually cause losses.

3:00So the rule is match the tool to how much error you can eat.

3:04Exactly.

3:05And here's the number that should scare people.

3:07Gartner, a research firm so treated as directional, has said for a while that a large share of

3:12privacy tech pilots never reach production.

3:16My own experience puts real production deployment in the single digits of the companies claiming

3:20they have it.

3:22The rest have a proof of concept and a press release.

3:25Name what everyone gets wrong in one sentence.

3:29Everyone treats these as products you buy and switch on.

3:32They're research programs you staff with cryptographers and performance engineers.

3:36And if you don't have those people, you have a very expensive science fair.

3:40What about secure multi-party computation?

3:43Several organizations jointly analyzing data without any of them seeing the others raw

3:47records?

3:49Scientists love that pitch for fraud rings.

3:51They do.

3:52And the technology genuinely works for narrow agreed upon questions.

3:56The killer isn't the math.

3:58It's the lawyers.

4:00Getting three competitors to sign off on exactly which computation runs takes 18 months

4:04before a single line executes.

4:07The bottleneck is legal and governance, not silicon.

4:11Nobody budgets for that.

4:12So someone's listening, a pilot on their desk, bored breathing down their neck.

4:16What do they do, Monday?

4:18Take your single highest risk data flow, the one that would end up on the news.

4:23And ask one question.

4:24What's the largest accuracy loss or slowest speed I can tolerate for that specific job?

4:30Write the number down first.

4:32Then and only then let a vendor near you.

4:34If they won't test against your number, they're selling you a demo.

4:38Done.

4:39Want to know where you actually stand?

4:40Take the CDO self-assessment at MBA-training.com.