The Role of Homomorphic Encryption in W3AI
Homomorphic encryption is an innovative cryptographic technique that is gaining increasing adoption across various industries thanks to the superior data protection it offers compared to traditional encryption techniques.
In this write-up, we explore the concept of homomorphic encryption and analyze the key role it will play in enhancing data privacy across the W3AI ecosystem.
Let's dive in:
Since the early days of computing, traditional encryption techniques such as symmetric and asymmetric encryption have done a pretty good job of protecting the data of users on the web.
However, these encryption techniques still have one critical flaw:
"An encrypted data needs to be decrypted and exposed to 3rd parties before it can be used for computational tasks."
This decryption process defeats the actual purpose of encryption since private data is still exposed to 3rd parties whom it is supposed to be protected from in the first place.
To address this critical flaw undermining the privacy of users, homomorphic encryption was proposed and developed over the last few decades. Let's take a look:
Homomorphic encryption is a cryptographic technique that converts encrypted data into "ciphertext" that can be analyzed and worked with as if it were still in its original form during computational tasks.
With homomorphic encryption, computation can be carried out on encrypted data by 3rd parties without needing to expose the data to them via decryption.
There are three major types of homomorphic encryption, which include:
▪️ Partially Homomorphic Encryption
▪️ Somewhat Homomorphic encryption
▪️ Fully Homomorphic Encryption (FHE)
Let's analyze the role homomorphic encryption will play in the W3AI ecosystem:
AIOZ Web3 AI (W3AI) is a decentralized AI-as-a-service infrastructure powered by 180,000+ global edge nodes in the AIOZ DePIN and a Web3-incentivized collaborative AI marketplace.
W3AI will deploy homomorphic encryption for various AI computational tasks carried out on the platform, including:
1.) AI Model Training: With homomorphic encryption, owners of AI training data can comfortably share their private data with AI researchers & businesses utilizing W3AI's infrastructure for AI model training.
For example, an individual can provide his/her encrypted medical records to a group of medical researchers utilizing W3AI's infrastructure to train an AI model capable of diagnosing a particular disease without the researchers needing to decrypt and access sensitive medical records directly.
2.) AI Inference: With homomorphic encryption, the AI researchers/developers and businesses who will utilize W3AI's infrastructure for inference tasks can do so without needing to access the data an inference task is being executed with.
For example, a loan company utilizing W3AI's infrastructure can run its AI model on the encrypted financial records of a loan applicant to determine his/her creditworthiness without first decrypting the records.
This form of protection will go a long way in ensuring that these entities do not potentially break many of the stringent data privacy laws that are found in various jurisdictions around the world today.
Aside from AI computational tasks, homomorphic encryption will also enable data owners who will utilize the W3AI marketplace to share their private data with the AI community to do so without any worries about exposing confidential data to the general public.
All of these will come together to guarantee the complete data privacy of users on W3AI while ensuring that a high level of trust is maintained across its ecosystem.
These benefits imply that homomorphic encryption will be one the most important features on the W3AI platform, and its potential role in catapulting W3AI to the forefront of decentralized AI computing cannot be understated.
To learn more about AIOZ W3AI ahead of its imminent release, you can download its vision paper at the link below:
aioz.network/w3ai
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