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
- š Big data laid the foundation for the current hype of artificial intelligence (AI), which has immense untapped potential.
- š Privacy concerns are a major obstacle in utilizing AI for analyzing sensitive health data and other personal information.
- š Fully homomorphic encryption (FHE) is a revolutionary technology that enables computation on encrypted data without compromising privacy.
- š¤ FHE allows encrypted data to be processed and returns an encrypted result that only the data owner can decrypt and read.
- š The concept of homomorphic encryption has been around since the 1970s, but it was only proven feasible in 2009 by Craig Gentry.
- š§ Despite its potential, FHE has not been widely adopted due to its high computational cost and the complexity of the encryption methods.
- š» Lattice cryptography is the approach used in current FHE methods, which produces very long strings that are challenging for standard CPUs and GPUs.
- š Specialized computer chips are being developed by companies like Intel, Chain Reaction, and Fabric Cryptography to support FHE more efficiently.
- š„ FHE could significantly benefit scientific research by enabling analysis of data currently restricted due to privacy regulations.
- š 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.
- š 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
š 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.
š 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
š”Big Data
š”Artificial Intelligence (AI)
š”Privacy Concerns
š”Fully Homomorphic Encryption
š”Homomorphisms
š”Lattice Cryptography
š”Quantum Computers
š”Intel
š”British National Health Service (NHS)
š”European Union (EU) Privacy Regulations
š”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 mightĀ think. But today's hype, artificial intelligence,Ā Ā
is built on big data's foundation. AndĀ we're just scratching the surface of AI'sĀ Ā
potential because of one major obstacle -Ā privacy concerns. Think about getting anĀ Ā
AI to analyse your health data to detectĀ issues early. It could save your life,Ā Ā
but you'd have to hand overĀ extremely personal information.Ā
Here's the amazing thing - new encryptionĀ technology makes it possible to get theĀ Ā
benefits of data analysis without that privacyĀ trade-off. It allows computation on your fullyĀ Ā
encrypted data and returns an encrypted resultĀ that only you can read. Letās have a look.
The technology in case is calledĀ fully homomorphic encryption, no wait,Ā Ā
donāt go. I know it sounds somewhat off-puttingĀ but the idea isnāt all that hard to understand.
Homomorphic has nothing to do with homoĀ sapiens , itās about homomorphisms,Ā Ā
that are mathematical operations which preserveĀ the relations of the structures theyāre acting on.Ā
A simple example of a homomorphism isĀ multiplication with a constant, say 7,Ā Ā
on the real numbers. Itās a homomorphism becauseĀ it preserves addition. You can either take twoĀ Ā
numbers, add them, and then use the multiplicationĀ with 7 , or multiply each number with seven,Ā Ā
and then add them. Same thing. So thisĀ map multiplication by 7 is a homomorphism,Ā Ā
from the real numbers onto itself. For the homomorphic encryption theĀ Ā
idea is similar. What you want is an encryptionĀ that works so that mathematical operations likeĀ Ā
addition and multiplication still work the sameĀ way. Say D is your data, you encrypt your dataĀ Ā
to E of D Then send this encrypted data to someoneĀ else. That other party does the calculation whichĀ Ā
you asked for. They get a result , send the resultĀ back to you, you unencrypt it. If the homomorphismĀ Ā
works as desired, that should give you theĀ same as if youād sent the unencrypted data.
The idea of homomorphic encryptionĀ has been around since the 1970s,Ā Ā
but it wasnāt until 2009 that CraigĀ Gentry, an American computer scientist,Ā Ā
proved that itās actually possible and alsoĀ came up with an encryption scheme. You knowĀ Ā
I think fully homomorphic encryption isnātĀ the most catchy name ever. If heād calledĀ Ā
it the Gentry Method, Iām sure by now theĀ birds would be chirping it from the trees.
So itās a fairly recent development. TheĀ reason it hasnāt been widely used so farĀ Ā
is that while it works, itās computationallyĀ extremely expensive. The methods that have soĀ Ā
far been produced use an approach called latticeĀ cryptography. But this type of encryption producesĀ Ā
a lot of very long strings and the CPUs andĀ GPUs that your standard computer runs on areĀ Ā
not well suited to working with them. SoĀ itās slow and it takes up a lot of energy.
And let me be clear that when I say the bitsĀ are long, I donāt mean theyāre 65 bits insteadĀ Ā
of 64. They can be tens of thousandsĀ of bits long. Indeed the encryptionĀ Ā
scheme is so complex itās known to be safeĀ from cracking even with quantum computers.
This is why there have in the past years a bunchĀ of companies working on developing computer chipsĀ Ā
especially for this purpose. This includesĀ Intel which is working together with DARPA,Ā Ā
and the Korean Electronics and TelecommunicationsĀ Ā
Research Institute but also startups likeĀ Chain Reaction and Fabric Cryptography .
Fabric has a chip that they say is readyĀ to go into mass production and will shipĀ Ā
in a few months time. Intel, too, saysĀ their chip is almost ready and the projectĀ Ā
will be completed later this year. TheĀ Israeli company Chain Reaction is alsoĀ Ā
almost ready to go on the market. And theĀ Korean ETRI says it has a chip already.
The idea is basically that companies usingĀ these chips can be trusted with your data.Ā Ā
Take for example the somewhat peculiar caseĀ of the British National Health Service whichĀ Ā
asked no other than Peter Thielās companyĀ Palantir to overhaul its data management. NotĀ Ā
everyone has been excited about this idea.Ā If fully holomorphic encryption was used,Ā Ā
itād be possible to work on patientās dataĀ while that data remains fully encrypted.
Fully homomorphic encryption could greatlyĀ benefit scientific research in general,Ā Ā
because it would be possible to analyse allĀ kinds of data that is currently out-of-boundsĀ Ā
because of privacy regulations, especially theĀ notoriously tight ones in the European Union.
I find this an amazing development becauseĀ itās such a nice example for the practicalĀ Ā
use of mathematics. Homomorphisms onĀ algebraic rings doesnāt sound like theĀ Ā
kind of mathematics that would be good forĀ anything much, but here we go. And itās notĀ Ā
that this maths has been known for centuriesĀ or such, itās an active area of research,Ā Ā
where the codes are still being developed.Ā Though if science communication on YouTubeĀ Ā
has taught me one thing itās that if you wantĀ to be cryptic, maths is your best friend.
If you become a member of our YouTube channel,Ā Ā
you get to see our science news videosĀ as soon as they are uploaded and,Ā Ā
in the "serious" tier we also have a weeklyĀ summary. It's an easy way to support our channelĀ Ā
and keep the videos coming, so go and have aĀ look. Thanks for watching, see you tomorrow.
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