We are aware of the fact that encrypted data is the heart of the decentralized ecosystem right? In the sense that there is a system that essentially relies on anonymity and privacy. And so naturally as the ecosystem grows we can easily assume that the need for performing computations on encrypted data with the power of AI algorithms has seen a rise.
This in turn has also brought up the charts for the demand for privacy-preserving systems, don’t we agree? This is where zkLLMs step in, my fellow degens!
Okay, let me tell you that if you and I were to enter a prompt to run a computation using an AI engine there can be two major things that would determine the quality of that computation. Want to know what are those?
Well, they are obviously the privacy (of the prompt provided, the parameter on which the computations happen and output data generated) and also mind you the efficiency of actually running that computation.
Zero Knowledge Proofs works for the privacy of the data so that it is preserved and talking about the Large Language Models (aka the trained Machine learning models) these basically ensure that the computation is done efficiently within seconds by an algorithm trained on an incredibly massive amount of data. Do you get it?
Zero-Knowledge Large Language Models (zkLLMs) are a way forward to keeping user data supremely private while utilizing powerful AI models you know. Built on none other than regular LLMs, ZK LLMs make use of zero-knowledge proofs (zkPs) to make sure the AI model processes user data correctly without ever actually seeing it.
Understanding Zero Knowledge Proofs (zkPs)
For the uninitiated let me tell briefly that Zero-Knowledge Proofs (ZKPs) is a powerful cryptographic tool that allows one party (the prover) to convince another party (the verifier) of the truth of a statement without revealing literally anything about the statement itself. Well imagine how precious it can be in situations where privacy is a concern, am I right?
To provide you degens an overview of what is the function on ZKPs, I’d say that they–
Preserve the Privacy of Our Data
Yes, the importance of zero-knowledge proofs lies in the fact that the prover never reveals the underlying information used to prove the statement. So it is comparable to proving you’re over 21 without showing your ID you know. In that situation, a ZK proof allows you to literally demonstrate this using a cryptographic protocol which ultimately ensures that your date of birth remains confidential.
Verify Our Computations
ZKPs have also proved to be important in verifying the accuracy of computations performed on sensitive data. Is it that crucial, you ask? Well yes because simply knowing the outcome doesn’t guarantee the computation was done correctly. I mean imagine a situation of outsourcing a complex financial calculation and a technology like zero-knowledge proof that allows the recipient to verify the answer without ever needing access to the original financial data.
Machine Learning and ZK Proofs: A Perfect Match
For us fellow degens living under a rock let me tell you that Machine Learning and Artificial Intelligence have been on fire lately and the likes of Google, Microsoft and Apple are all turning to this new tech.
Machine learning is essentially where algorithms are trained on different data sets to make predictions or answer queries you know. These algorithms learn from a large set of data and ZK proofs can help them protect the privacy of the data.
Now the interjection of zkP + ML = zkML is way too cool to be missed out on. zkML allows the machine learning model to learn from the data provided without you know it being ever exposed for privacy and security reasons.
Traditionally speaking training machine learning models often naturally required access to incredibly vast amounts of data which may contain sensitive information. And this is where zk proofs stepped in to bridge the gap of privacy (as well as data security) in providing sensitive information by allowing these models to be trained and used without revealing the underlying data.
Now you might wonder what are LLMs then? Well Large Language Models aka LLMs are a kind of Machine Learning Models which are trained on certain data of certain logics.
Let’s get a deeper understanding of LLMs before we see how they interact with zk Proofs, shall we?
What Are Large Language Models?
Well, Large Language Models (or LLMs) are machine learning models that are you know explicitly trained on incredibly huge amounts of data to understand the complexities of language. These ML models use deep learning techniques to process and analyze natural language text and this understanding ultimately leads them to generate coherent and contextually relevant responses to our queries or prompts.
LLMs are used in a wide range of applications including language translation, text generation and sentiment analysis. Some well-known examples of LLMs include:-
- GPT-3.5/4
- BERT
- Transformer models
While we can agree to the fact that machine learning has been going rogue it is also agreeable that combining the fruits of Zero Knowledge Proofs and LLMs can bring unimaginable feats to the computer world. And zkLLM for the real world is like hiring a digital accountant or an assistant with the help of whom you can get your accounts done and books maintained but you don’t even have to expose your data and neither does the virtual assistant reveal their trade secrets (algorithm).
Now join me in the journey of learning how this synergy between ZK Proofs and LLMs works!
zkP + LLM = zkLLM, What Is It?
You know ZK proofs can be used by LLMs to prove the existence of non-explicitly programmable properties of the input. I know that sounds intimidating. Well, what I’m trying to say is that an LLM could prove a sentence conveys a specific sentiment (e.g., sarcasm) without revealing the underlying algorithms used to determine that sentiment you know. This opens doors for more nuanced and privacy-preserving applications of LLMs.
While ZK proofs are important enough for proving the correctness of computations in zkLLMs, they alone cannot handle the complex computations required by these powerful AI models. This is where Fully Homomorphic Encryption (FHE) comes into play.
Fully Homomorphic Encryption: Computing on Encrypted Data
Fully Homomorphic Encryption is a cryptographic technique, by the use of which computations can be performed on encrypted data directly. Yes, you heard it right! Now computers have the super power to perform computations without decrypting the data itself.
The FHE method ensures the security of the data throughout the computation process. This technique has the potential to revolutionize the field of secure computing. Tt enables computations on sensitive data to be performed on data without ever revealing the underlying information itself.
Imagine a locked box with special properties. You can put your data inside and perform computations without opening the box. FHE allows zkLLMs to do calculations or operate on encrypted user data. The LLM can perform complex tasks like sentiment analysis or text generation on the encrypted data itself, without ever decrypting it.
When it comes to saving privacy, zkPs and FHE work hand in hand and complement each other working towards the same goal.
zkP and FHE: The Ultimate Powerhouse of Privacy Preserving
Zero-knowledge proofs (zkPs) and Fully Homomorphic Encryption (FHE) are two powerful tools that when used together can create a privacy-preserving powerhouse.
As we already know zkPs allow one party (the prover) to prove to another party (the verifier) the truth of a statement, without revealing any additional information about the statement itself. FHE on the other hand, allows computations to be performed directly without decrypting the data. This is useful in situations where privacy is a concern.
ZK proofs and FHE form the backbone of zkLLMs. Here’s how they work together:-
- FHE encrypts the user data. This ensures the data remains safe throughout the process.
- The user performs asks the LLM to perform computations on the encrypted data. zkLLMs are designed to work efficiently with FHE for these computations.
- ZK proofs prove that the asked queries/computations were solved correctly. The LLM proves it processed the data as instructed without revealing the data or the intermediate steps.
As we go deeper into understanding zkLLMs, one thing we need to keep in mind is the value of zkLLMs is created because decisions based on vast amounts of data are made securely and privately within seconds. To quote again ‘Within Seconds’.
The use of LLMs is of no value if the decisions are taking more time than they’re supposed to. Combining LLMs with zkProofs can sometimes cause an overload, making the time taken to perform a particular computation long.
Don’t scratch your head over it though. The answer to the problem we are facing it lies in some brilliant data quantization mechanisms that help us reduce the presented data to fewer bits ultimately improving efficiency and getting us back to speed.
What are Quantization Techniques & How do they work?
Data Quantization techniques are like data compression methods. These methods essentially reduce the number of bits required to carry a piece of information. This translates to smaller file sizes and faster processing. So to keep it sweet and simple, quantization plays a crucial role in improving the efficiency of zkLLMs.
Here’s how quantization techniques work.
zkLLMs work with large stacks of data to deliver intelligent responses. However, the movement of this data and processing of this data can be time-consuming and expensive. Quantization techniques my friends, are the angels in this disguise. These techniques shrink the size of this data so that fewer bits are required to carry that data.
For Example:-
- Numbers can be rounded or compressed to a smaller range to reduce the size of detail
- Similar data points can be grouped and assigned a single codeword
Once smaller data packers are created, this enables faster movement of the data. This means less data to store means less space needed, and less data to process translates to faster computation and lower power consumption for zkLLM operations.
Cerberus Squeezing (along with zkLLMs) is one such method used by the BasedAI network in their approach to speed up the computations made by their AI network engines.
Hitesh an active member of the crypto community, brilliantly explained on X platform how BasedAI is a prime example of the the excellence of zkLLMs. Hitesh went on to explain that
BasedAI is a system that allows Large Language Models (LLMs) to process information while keeping user data confidential.
The company achieved this through a combination of Fully Homomorphic Encryption (FHE) and a technique called Cerberus Squeezing. With FHE the data stays encrypted even during computations and Cerberus Squeezing ensures that the process is done with efficiency and avoids and slowdowns.
BasedAI creates a decentralized network where users interact with miners and validators through “Brains”. These Brains are basically nothing but containers that can run modified LLMs. The network gives incentives to partners in order to maintain high performance through a reward system based on $BASED tokens.
Some Other famous techniques include Uniform, Non-uniform, Scalar and Vector Quantization.
Now that we have determined how zkLLMs work we have solved the problem of efficiency. Let’s take a look at some real-world use cases for zkLLMs.
Real-Life Use Cases of zkLLM
In today’s world privacy and powerful AI capabilities are both necessary. zkLLMs are a relatively new concept, but their applications hold some promise for real-life situations.
Here let’s take a look at some real-life situations where zkLLMs can be applied:-
Secure Medical Diagnosis
zkLLMs can be very useful in the healthcare industry. Suppose a patient uploads their medical records to a Cloud-based AI system. zkLLMs could analyze the data while keeping the sensitive patient information still encrypted and still identify any potential health issues. This protects patient privacy while allowing for advanced AI-powered diagnosis.
Personalized Financial Analysis
One of the primary uses of zkLLMs could be to analyze user’s encrypted financial data. Data from bank statements and investment portfolios can be scanned by the LLMs and asked to provide financial advice based on that. The LLM could identify investment opportunities without ever decrypting the financial information.
Secure Chatbots and Virtual Assistants
zkLLMs can also be used to power chatbots and virtual assistants. These bots can solve the queries of a user magically within seconds! What’s more?, the query still remains encrypted which means that the privacy of the user is maintained as well.
Private Content Moderation
Another great application of zkLLMs could be to analyze, identify and remove harmful or inappropriate content on the internet. The Large language Model operates on the encrypted chats to identify any violations or inappropriate data. On the other hand, ZK proofs can be used to show that the chats were scanned correctly.
Secure Scientific Research
zkLLMs can be used to analyze sensitive scientific data such as genomic data and other research findings.
Conclusion
Zero-knowledge Large Language Models (zkLLMs) show that AI has taken a significant leap forward by offering language models that prioritize user privacy. Combining the strengths of zero-knowledge proofs (zkPs) and Fully Homomorphic Encryption zkLLMs ensures that computations are performed correctly in a secure manner.
That’s amazing, isn’t it? this outright opens doors to a wide range of privacy-preserving applications across various sectors. Even though the zkLLM technology is still in its early stages, the potential benefits are undeniable. As the technology moves forward, it makes way for a more secure and privacy-aware digital future.
