CyberWire Daily - Keeping data confidential with fully homomorphic encryption. [Research Saturday]

Episode Date: March 13, 2021

Guest Dr. Rosario Cammarota from Intel Labs joins us to discuss confidential computing. Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names — multiparty computing, federated learning and privacy-preserving analytics, among them. Confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance. The research and supporting documents can be found here: Intel Labs Day 2020: Confidential Computing Confidential Computing Presentation Slides Demo video Learn more about your ad choices. Visit megaphone.fm/adchoices

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That's ai.domo.com. Hello, everyone, and welcome to the CyberWire's Research Saturday. I'm Dave Bittner, and this is our weekly conversation with researchers and analysts tracking down threats and vulnerabilities, solving some of the hard problems of protecting ourselves in a rapidly evolving cyberspace. Thanks for joining us.

So, fully homomorphic encryption is an encryption technique. But unlike the type of encryption that we use right now, homomorphic encryption allows to keep confidentiality of data while data is being in use. That's Dr. Rosario Camarota. He's a principal engineer at Intel Labs. The research we're discussing today is titled Confidential Computing, Advances in Federated Learning and Fully Homomorphic Encryption. And now, a message from our sponsor, Zscaler, the leader in cloud security.

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When a message is encrypted into a ciphertext, which we will refer to as a cryptogram right now, if the cryptogram is homomorphically encrypted, you can actually manipulate its content without decrypting it. And what's different with the homomorphic encryption, what homomorphic encryption adds to what we do right now is that homomorphic encryption allows to keep confidentiality of data while data is being in use. Because you can compute on the content of the cryptogram without the decryption. So give me an example of where this would apply. What's the use case for this? of where this would apply?

What's the use case for this? Nowadays, two of the main emerging areas that we are seeing today are data collaborations and intelligent automation that relies on data collaborations to perform automatically more and more intelligent and personalized decisions

based on data extracted from patterns. So when collaborations happen across mistrust entities, basically these entities aim to collaborate more and more, then there is the problem of can we share the data? How do we share the data? What data do we share? And part of the roadblockers in data sharing concern privacy, because much of the digital data out of which you would like to extract patterns include sensitive and private data. So we're talking about potentially, could that include things like medical information? could that include things like medical information?

Absolutely. If you think, for example, to automation in the medical space, let's think, for example, to a tumor segmentation model that is served in the cloud. What that helps to do is to increase the rate of scans that you can analyze. And that's very important because timeliness in that context may save lives. So now the problem there is that if you are outsourcing scans to a service that is deployed on the cloud, you need to protect the privacy of these scans. And when we are talking

about privacy, definitely we have the following two things. So one is basically the association of the scan with the patient. And the other is the results of the analysis. Well, let me ask you sort of a basic and perhaps a question that demonstrates my ignorance when it comes to the topic. So we're talking about fully homomorphic encryption. Is there partially homomorphic encryption? Yes. Actually, that's an excellent question.

Yes. Oh, good. There are many flavors of it. There is partial homomorphic encryption. There is something else that is somewhat homomorphic encryption, and there is fully homomorphic encryption. Let me tell you a little bit, very briefly, about the difference between those. With partial homomorphic encryption, you can basically perform only one type of operations on cryptograms. So it's either additions or multiplications.

With somewhat homomorphic encryption, you can perform both addition and multiplications. With somewhat homomorphic encryption, you can perform both addition and multiplications, but for functions up to a certain complexity. And in fact, when you have a cryptosystem that allows to perform operation on cryptograms and it can perform both addition and multiplications, the first question that you ask, is this fully homomorphic encryption?

And then the answer usually is it's somewhat, because you can only handle up to a certain complexity. Fully homomorphic encryption extend, and the majority of the constructions that are known today, somewhat homomorphic encryption schemes with the ability of performing arbitrary computation of arbitrarily complex functions. Now, my understanding is that this is very computationally complex, correct? Yes, it is. And that's a barrier for adoption. It is one of the barriers for adoption, yes. adoption it is one of the barriers for adoption yes um so to speak any encryption technique um the encryption process is in in any encryption techniques the encryption process is inherently inefficient what that means is that there is an expansion of um the original um data type size when you generated the cryptograms. In homomorphic encryption, expansion can be 100 to 1,000 times,

can generate 100 to 1,000 times larger cryptograms. And if you think to handle this type of data on existing platforms, you start already having an idea of how even doing simple computation on very large cryptograms can be more stressful with respect to both computation, computational resources, memory management,

and communication between the host processor and the computational resources, basically memory transfer. You know, I grew up, when I was a kid, I remember it was when the Rubik's Cube first came out. And everyone was fascinated with it. It was a big hit. And there were books that you could buy to help you solve. If you wanted to learn how to solve a Rubik's Cube, there were books that had step-by-step instructions. And in the early days,

those books might take you half an hour or so to solve a Rubik's Cube. These days, if you go on YouTube, you can see these kids today are solving Rubik's Cubes in seconds. And I think a big part of that is that over time, the algorithms have gotten so much more efficient when it comes to being able to do that. Is that sort of thing happening with fully homomorphic encryption as well?

Are researchers like you and the folks at Intel Labs, clever humans who are banging away at this, are you coming up with more efficient ways to come at this problem? So that's a very interesting question. Well, crypto systems usually are designed to protect the data for a certain amount of time. And so homomorphic encryption as crypto system by itself is being designed for the same purpose. And so to speak, the complexity that is required to break a cryptosystem is usually very high, even at the lowest level of compliance when you deploy a cryptosystems such that in 10 years with the majority of it, with all the resources that you have available right now, or more than 10 years, you want

to be able basically to break the cryptosystem. Now for what concerns homomorphic encryption, homomorphic encryption is an additional property in terms of protection because it's foundationally based on a mathematics that would be resistant even against the crypto analysis with quantum algorithms that is going to be the next type of big threat to the current cryptography. What about on the hardware side of things? I mean, obviously, you know, Intel is a big innovator and manufacturer of processing hardware as well. And we've been seeing this trend over the past few years of having, you know, dedicated parts of chips that are designed to do difficult things in a very efficient way.

Is this an area of research as well where we could see certain types of hardware that were dedicated to this task? Yes. So the main driver toward the specialization of a hardware, toward very specific tasks. So one example that comes to mind in the modern days is basically specialized hardware for artificial intelligence, is to make sure that your hardware can run the tasks very, very specifically, keeping in mind that your task is processing certain data types. In this case, when we go to cryptography, there are already instances of accelerators that are more suitable than general purpose hardware to execute cryptography. And in fact, even within processors, you may see that there are instruction set extensions that are dedicated to process

cryptogram for the cryptography that is deployed nowadays. Now, similarly, for homomorphic encryption, being mindful that the cryptogram are a lot more complex, you would need some form of specialized hardware to reduce all the computational overhead that you mentioned earlier. What about the larger world of research when it comes to these sorts of things? I'm thinking of establishing standards for this. Where are we in terms of standards bodies and being sure that these sorts of

encryption methods can be used broadly? Yes. There has have been a group participated by universities and industries called the homomorphic encryption.org that started basically to lay out the foundational work for the standardization

in terms of security parameters. So as we know, any crypto systems is something that is parameterized to some secrets. And the length of the secrets, so to speak, grossly indicates the resistance of the crypto system to algebraic attacks. attacks. Now, what happens is that for the mathematics that is below cryptosystems that allow you to compute and encrypt data, this group has been looking into the security of the instantiation of the mathematical fields underneath this cryptography and very recently we started the exporting, basically this work and making

it more visible to the global community by working with the international standards. It is very important. And I would say it's a fundamental for the whole industry to have standards about crystal cryptography, as you correctly point out. And that basically includes best practices, what is the best selection of the parameters for certain use cases. But one difference that makes homomorphic encryption unique is that, unlike traditional cryptography, in homomorphic encryption, there is an entanglement between the application domain, the workload, and the cryptography itself that otherwise would not be connected together. And the reason for that is because you are computing on encrypted data.

So the standards in part is application domain plus cryptography together. plus cryptography together. Help me understand, is there a concern that folks may be able to infer the data from the calculations they're doing on the data? No, for two reasons. What you can infer during an homomorphic encryption operations with traditional methods basically to leak data is ciphertext by itself. And the fact that you are using an homomorphic encryption system as an additional advantage that you don't need to store decryption keys on the system, which is an additional kind of target of attacks.

So the only information that an attacker would gain by introducing, by monitoring the channel, so to speak, would be ciphertext. It can use that ciphertext, but it cannot look into it. For what concerns

looking at the output of a computation, homomorphic encryption systems, the encryption procedure is inherently non-deterministic. And so what it means is that if you encrypt the same data twice and then you process this data, the output of the computation is different. and then you process this data, the output of the computation is different, is encrypted, but is also different. So it has this property that disambiguates, so to speak, inferring the result of the operations and also inherently protects the intermediate data.

Wow. Well, as you look towards the future, as this technology makes its way down and becomes more practical for everyday use, and there are broader applications as we're able to make use of it, as both the hardware and the developments that folks like you are working on, how do you see that affecting us in day-to-day lives? What are the advantages when it comes to privacy and security that folks are going to see as a result of this making its way out into the general use? Yeah, let me give you an example that clarifies things. So currently, when we go around with our mobile devices

and we enter an environment that is progressively smarter, one thing that happens or that we should start seeing more and more is that we are going to receive personalized information from that environment. And either in our mobile phone or other gadgets that basically interact with the environment. The environment becomes a cyber-physical system, so to speak, and it's intelligent because there is all this machine learning. Now, in order to provide you a personalized recommendation, which is supposed to do good to you,

the system needs to ingest some of the information that you are carrying with you, such as your location, if you are carrying with you, such as your location, if you are making a transaction, your credit card information, other aspects of the transaction, what you have purchased, why you should be looking into another shelf within the same store, because there is something that potentially is going to help you, where you should shop today, all these type of things. So in order to perform that personalization, the system that is performing this type of computation needs to consume your data. With the homomorphic encryption, it will be able to consume the data without actually

seeing the data. So any unintended use of your data potentially cannot happen. And so you are receiving the personalization, but you are not giving up your data. For you personally, it sounds like this stuff is a lot of fun. I mean, it seems like you and your team there at Intel Labs, this is the kind of, you know, it may be baffling for folks like me who are more mathematically challenged, but it does seem like, you know, these challenges, it is a

lot of fun for you and your team, isn't it? It is, it is. There are many challenges behind it. Some are from on the mathematical side. The research around the homomorphic encryption is still progressing. And in fact, we do have several key players at universities worldwide to continue making research for making homomorphic encryption systems more efficient from an algorithmic perspective while retaining the same level of security. That part is actually really hard, but at the same time is really challenging. Now, let me give you the perspective of a person

that also sits within the semiconductor industry. We talked about how processing these cryptograms is actually challenging, primarily because of their size, but also because the operations that you do in order to manipulate the content of cryptograms is also more complex than just doing additional multiplications of plain text data, right? So when you actually envision basically a computer architecture that natively can process these cryptograms, a lot of challenges emerge because of how different is the cryptogram from the native data types that we are used to see nowadays.

So there are a lot of challenges and a lot of excitements from the point of view of the technology. There is excitement in the ecosystem because applications of this technology can benefit humanity. And that's the part, since you asked personally, yes, it is fun, but the real goal is that, well, if we make it happen, humanity benefits from it. And that aspect is fulfilling. It's one of

the missions, actually, that we at Intel Labs, as a research lab, have and pursue as we keep doing research. Our thanks to Dr. Rosario Camarota for joining us. The research is titled Confidential Computing, Advances in Federated Learning and Fully Homomorphic Encryption. We'll have a link in the show notes.

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