This Obscure Maths Will Revolutionize Data Privacy
Summary
TLDRThe video script discusses the potential of fully homomorphic encryption (FHE) in the context of big data and AI, highlighting its ability to enable data analysis without compromising privacy. FHE allows computations on encrypted data, with results that can only be decrypted by the data owner. Despite its theoretical existence since the 1970s, the technology is computationally intensive and has been slow to adopt. Recent developments include specialized chips from companies like Intel, Chain Reaction, and Fabric Cryptography, aiming to make FHE more practical for applications such as secure health data analysis and scientific research, potentially revolutionizing how sensitive data is handled in the face of stringent privacy regulations.
Takeaways
- 🌟 Big data laid the foundation for the current hype of artificial intelligence (AI), which has immense untapped potential.
- 🔒 Privacy concerns are a major obstacle in utilizing AI for analyzing sensitive health data and other personal information.
- 🔐 Fully homomorphic encryption (FHE) is a revolutionary technology that enables computation on encrypted data without compromising privacy.
- 🤔 FHE allows encrypted data to be processed and returns an encrypted result that only the data owner can decrypt and read.
- 📈 The concept of homomorphic encryption has been around since the 1970s, but it was only proven feasible in 2009 by Craig Gentry.
- 🚧 Despite its potential, FHE has not been widely adopted due to its high computational cost and the complexity of the encryption methods.
- 💻 Lattice cryptography is the approach used in current FHE methods, which produces very long strings that are challenging for standard CPUs and GPUs.
- 🔋 Specialized computer chips are being developed by companies like Intel, Chain Reaction, and Fabric Cryptography to support FHE more efficiently.
- 🏥 FHE could significantly benefit scientific research by enabling analysis of data currently restricted due to privacy regulations.
- 🌍 The adoption of FHE could allow companies to be trusted with sensitive data, as exemplified by the British National Health Service's collaboration with Palantir.
- 📚 Homomorphisms on algebraic rings, the mathematical basis of FHE, demonstrate the practical applications of an active area of research in modern technology.
Q & A
What is the main obstacle in harnessing the full potential of artificial intelligence due to big data?
-The main obstacle is privacy concerns, as using AI to analyze personal data like health records could save lives but requires sharing extremely personal information.
How does new encryption technology address the privacy issue in AI data analysis?
-New encryption technology, specifically fully homomorphic encryption, allows for computations on fully encrypted data and returns an encrypted result that only the data owner can decrypt, thus preserving privacy.
What is fully homomorphic encryption and how does it relate to homomorphisms in mathematics?
-Fully homomorphic encryption is a type of encryption that allows computations on encrypted data. It is related to homomorphisms, which are mathematical operations that preserve the relations of the structures they act upon.
Who is Craig Gentry and what is his contribution to the field of homomorphic encryption?
-Craig Gentry is an American computer scientist who proved the possibility of fully homomorphic encryption and developed an encryption scheme in 2009.
Why hasn't fully homomorphic encryption been widely adopted yet?
-It hasn't been widely adopted because the computational process is extremely expensive, slow, and energy-intensive due to the use of lattice cryptography and the production of very long strings.
What are some companies and institutions working on specialized chips for homomorphic encryption?
-Companies like Intel, working with DARPA, the Korean Electronics and Telecommunications Research Institute, and startups such as Chain Reaction and Fabric Cryptography are developing specialized chips for this purpose.
What is the significance of developing specialized computer chips for homomorphic encryption?
-Specialized chips can handle the complex computations required for homomorphic encryption more efficiently, making it practical for real-world applications and enabling companies to securely process encrypted data.
How could fully homomorphic encryption benefit scientific research?
-It could allow researchers to analyze data that is currently restricted due to privacy regulations, enabling new insights and discoveries without compromising individual privacy.
What is the status of the specialized chips mentioned in the script?
-Fabric Cryptography claims to have a chip ready for mass production, Intel's chip is almost ready, and the Korean ETRI states it already has a chip available.
How does the development of fully homomorphic encryption exemplify the practical use of mathematics?
-It demonstrates how abstract mathematical concepts, like homomorphisms on algebraic rings, can be applied to solve real-world problems, such as data privacy in AI and scientific research.
What is the relevance of homomorphic encryption in the context of quantum computing?
-The encryption scheme is so complex that it is considered safe from cracking even by quantum computers, which are expected to break many current encryption methods.
Outlines
🔐 Overcoming Privacy Hurdles with Homomorphic Encryption
This paragraph discusses the intersection of big data and artificial intelligence, highlighting the challenges posed by privacy concerns. It introduces fully homomorphic encryption as a solution that allows for data analysis without compromising privacy. The concept is explained through the mathematical notion of homomorphisms and the practical example of multiplication with a constant. The paragraph also touches on the history and development of homomorphic encryption, mentioning Craig Gentry's contribution in 2009. It acknowledges the computational expense and the ongoing efforts by companies like Intel, Chain Reaction, and Fabric Cryptography to develop specialized chips for this purpose. The potential applications in healthcare and scientific research are also highlighted, emphasizing the transformative impact of this technology on handling sensitive data.
📚 The Practical Wonders of Mathematical Research
The second paragraph shifts focus to the practical applications of mathematical research, particularly in the context of homomorphisms on algebraic rings. It emphasizes the relevance of this seemingly abstract mathematics to real-world problems, noting that it is an active area of research with ongoing code development. The paragraph concludes with a call to action for viewers to support the YouTube channel for more science news and updates, reinforcing the importance of science communication.
Mindmap
Keywords
💡Big Data
💡Artificial Intelligence (AI)
💡Privacy Concerns
💡Fully Homomorphic Encryption
💡Homomorphisms
💡Lattice Cryptography
💡Quantum Computers
💡Intel
💡British National Health Service (NHS)
💡European Union (EU) Privacy Regulations
💡Science Communication
Highlights
Artificial intelligence is built on the foundation of big data.
A major obstacle to AI's potential is privacy concerns.
New encryption technology enables data analysis without privacy trade-offs.
The technology allows computation on encrypted data and returns an encrypted result.
Fully homomorphic encryption is the solution that enables secure data analysis.
Homomorphic encryption is based on mathematical operations that preserve the relations of the structures they act on.
The concept of homomorphic encryption has been around since the 1970s.
Craig Gentry proved the possibility of fully homomorphic encryption and developed an encryption scheme in 2009.
The technology is computationally expensive and slow, requiring specialized hardware.
Lattice cryptography is the approach used by current homomorphic encryption methods.
Companies like Intel, Chain Reaction, and Fabric Cryptography are developing specialized chips for homomorphic encryption.
These chips could allow companies to be trusted with sensitive data.
Fully homomorphic encryption could revolutionize scientific research by enabling analysis of currently restricted data.
The practical application of this mathematics is a significant development in the field.
Homomorphisms on algebraic rings are an active area of research with ongoing code development.
The development of fully homomorphic encryption is an example of the practical use of advanced mathematics.
The technology is a breakthrough in data security and privacy, safe even from quantum computing.
The British National Health Service's data management overhaul by Palantir could have benefited from this encryption.
Transcripts
Big data was yesterday's hype, or so you might think. But today's hype, artificial intelligence,
is built on big data's foundation. And we're just scratching the surface of AI's
potential because of one major obstacle - privacy concerns. Think about getting an
AI to analyse your health data to detect issues early. It could save your life,
but you'd have to hand over extremely personal information.
Here's the amazing thing - new encryption technology makes it possible to get the
benefits of data analysis without that privacy trade-off. It allows computation on your fully
encrypted data and returns an encrypted result that only you can read. Let’s have a look.
The technology in case is called fully homomorphic encryption, no wait,
don’t go. I know it sounds somewhat off-putting but the idea isn’t all that hard to understand.
Homomorphic has nothing to do with homo sapiens , it’s about homomorphisms,
that are mathematical operations which preserve the relations of the structures they’re acting on.
A simple example of a homomorphism is multiplication with a constant, say 7,
on the real numbers. It’s a homomorphism because it preserves addition. You can either take two
numbers, add them, and then use the multiplication with 7 , or multiply each number with seven,
and then add them. Same thing. So this map multiplication by 7 is a homomorphism,
from the real numbers onto itself. For the homomorphic encryption the
idea is similar. What you want is an encryption that works so that mathematical operations like
addition and multiplication still work the same way. Say D is your data, you encrypt your data
to E of D Then send this encrypted data to someone else. That other party does the calculation which
you asked for. They get a result , send the result back to you, you unencrypt it. If the homomorphism
works as desired, that should give you the same as if you’d sent the unencrypted data.
The idea of homomorphic encryption has been around since the 1970s,
but it wasn’t until 2009 that Craig Gentry, an American computer scientist,
proved that it’s actually possible and also came up with an encryption scheme. You know
I think fully homomorphic encryption isn’t the most catchy name ever. If he’d called
it the Gentry Method, I’m sure by now the birds would be chirping it from the trees.
So it’s a fairly recent development. The reason it hasn’t been widely used so far
is that while it works, it’s computationally extremely expensive. The methods that have so
far been produced use an approach called lattice cryptography. But this type of encryption produces
a lot of very long strings and the CPUs and GPUs that your standard computer runs on are
not well suited to working with them. So it’s slow and it takes up a lot of energy.
And let me be clear that when I say the bits are long, I don’t mean they’re 65 bits instead
of 64. They can be tens of thousands of bits long. Indeed the encryption
scheme is so complex it’s known to be safe from cracking even with quantum computers.
This is why there have in the past years a bunch of companies working on developing computer chips
especially for this purpose. This includes Intel which is working together with DARPA,
and the Korean Electronics and Telecommunications
Research Institute but also startups like Chain Reaction and Fabric Cryptography .
Fabric has a chip that they say is ready to go into mass production and will ship
in a few months time. Intel, too, says their chip is almost ready and the project
will be completed later this year. The Israeli company Chain Reaction is also
almost ready to go on the market. And the Korean ETRI says it has a chip already.
The idea is basically that companies using these chips can be trusted with your data.
Take for example the somewhat peculiar case of the British National Health Service which
asked no other than Peter Thiel’s company Palantir to overhaul its data management. Not
everyone has been excited about this idea. If fully holomorphic encryption was used,
it’d be possible to work on patient’s data while that data remains fully encrypted.
Fully homomorphic encryption could greatly benefit scientific research in general,
because it would be possible to analyse all kinds of data that is currently out-of-bounds
because of privacy regulations, especially the notoriously tight ones in the European Union.
I find this an amazing development because it’s such a nice example for the practical
use of mathematics. Homomorphisms on algebraic rings doesn’t sound like the
kind of mathematics that would be good for anything much, but here we go. And it’s not
that this maths has been known for centuries or such, it’s an active area of research,
where the codes are still being developed. Though if science communication on YouTube
has taught me one thing it’s that if you want to be cryptic, maths is your best friend.
If you become a member of our YouTube channel,
you get to see our science news videos as soon as they are uploaded and,
in the "serious" tier we also have a weekly summary. It's an easy way to support our channel
and keep the videos coming, so go and have a look. Thanks for watching, see you tomorrow.
Voir Plus de Vidéos Connexes
5.0 / 5 (0 votes)