This Obscure Maths Will Revolutionize Data Privacy

Summary

The 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

00:00

🔐 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.

05:01

📚 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.

Keywords

💡Big Data

Big Data refers to the large volume of data that is generated, collected, and analyzed to uncover insights and patterns. In the context of the video, it is the foundation upon which artificial intelligence (AI) operates, highlighting its importance in modern technology and data analysis. The script mentions that AI is built on big data's foundation, indicating its crucial role in the development and application of AI technologies.

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) is the development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation. The video emphasizes AI's dependence on big data and the potential it has due to advancements in encryption technology, which addresses privacy concerns. AI's role is pivotal in analyzing encrypted health data to detect issues early, potentially saving lives.

💡Privacy Concerns

Privacy concerns refer to the issues and risks associated with the collection, storage, and use of personal information. In the video, these concerns are highlighted as a major obstacle in the advancement of AI, particularly when it comes to analyzing sensitive health data. The development of fully homomorphic encryption is presented as a solution to these concerns, allowing data analysis without compromising individual privacy.

💡Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a form of encryption that allows computations to be performed on encrypted data without the need for decryption. The result of these computations remains encrypted and can only be read by the data owner. This technology is crucial in the video's narrative as it enables the analysis of sensitive data, such as health information, without exposing it to potential privacy breaches.

💡Homomorphisms

Homomorphisms are mathematical functions that preserve the structure of the data they operate on, meaning that the relationships between elements are maintained even after the transformation. In the context of the video, homomorphisms are foundational to the concept of fully homomorphic encryption, where the encryption allows for operations like addition and multiplication to be performed directly on encrypted data.

💡Lattice Cryptography

Lattice Cryptography is a method of encryption based on the mathematical concept of lattices, which are multi-dimensional grids of points. It is used in the development of fully homomorphic encryption and is known for its computational intensity, resulting in long strings of data that are challenging for standard CPUs and GPUs to process. Despite its complexity, lattice-based encryption is considered secure against quantum computing attacks.

💡Quantum Computers

Quantum computers are advanced computational devices that use the principles of quantum mechanics to process information. They have the potential to solve complex problems much faster than classical computers. In the video, it is mentioned that the encryption schemes developed for fully homomorphic encryption are so complex that they are considered safe from cracking, even by quantum computers, highlighting the robustness of the encryption methods.

💡Intel

Intel is a leading technology company known for its computer processors. In the context of the video, Intel is working on developing specialized computer chips designed to handle the computational demands of fully homomorphic encryption. This collaboration with DARPA indicates a significant industry interest in advancing encryption technologies for data privacy.

💡British National Health Service (NHS)

The British National Health Service (NHS) is the publicly funded healthcare system in the United Kingdom. In the video, the NHS is mentioned as an example of an organization that has entrusted its data management to Palantir, a company founded by Peter Thiel. The use of fully homomorphic encryption could have allowed the NHS to maintain the privacy of patient data while still enabling data analysis for improvements in healthcare.

💡European Union (EU) Privacy Regulations

The European Union (EU) Privacy Regulations refer to the set of laws and guidelines that protect the privacy and personal data of individuals within the EU. These regulations are notably strict, often preventing the use of certain types of data for research and analysis due to privacy concerns. The video suggests that fully homomorphic encryption could enable scientific research to access and analyze data that is currently restricted by these regulations without compromising privacy.

💡Science Communication

Science communication is the process of conveying scientific ideas, concepts, and findings to a broader audience, typically through various media platforms. In the video, the speaker reflects on the role of mathematics in science communication, particularly its utility in maintaining secrecy and encryption, which is essential for the development and application of technologies like fully homomorphic encryption.

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.

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