Ever since the launch of ChatGPT, countless AI tools have been launched. Many bring great solutions, but there’s a downside: data privacy. These AI models need data to be trained on, but with lots of data come lots of problems.Luckily, there’s a solution: Privasea. In this article, we’ll give you an overview of what the Privasea AI Network is, how it works, and what problems it solves. Let’s get into it!
The Privasea AI Network is a powerful system designed to prioritize the privacy and security of data throughout the AI computation process. It uses an innovative technology called Fully Homomorphic Encryption (FHE), which enables computations to be conducted on encrypted data, producing results that are identical to computations performed on unencrypted data.
What does this mean? Well, that sensitive information can be processed without ever being exposed in its original form. By leveraging this cutting-edge approach, the network facilitates collaborative AI processing among multiple parties while ensuring the confidentiality of sensitive information.
One of the key goals of the Privasea AI Network is to adhere to data protection regulations, including the stringent General Data Protection Regulation (GDPR) in the European Union. These regulations impose rigorous requirements on the collection, processing, and storage of personal data.
To comply with these regulations, organizations can employ privacy-preserving AI techniques like Fully Homomorphic Encryption to guarantee the protection of personal data throughout model training and inference.
In addition to regulatory compliance, another crucial objective of the Privasea AI Network is to safeguard users' sensitive data against unauthorized access. By using FHE to encrypt sensitive data during AI processing or inferencing period, the network acts as a formidable barrier against data breaches and unauthorized entry.
Furthermore, by employing privacy-preserving techniques to safeguard personal data during machine learning, the network cultivates trust in machine learning systems, encouraging individuals to willingly share their data.
The Privasea AI network consists of four main components: the HESea Library, the Privasea API, Privanetix and the Privasea Smart Contract Kit. Let’s take a look at what each of them does.
At the heart of the Privasea AI Network lies the HESea Library, which hosts an impressive collection of highly efficient implementations of popular Fully Homomorphic Encryption schemes like TFHE, CKKS, BGV, BFV and more.
This open-source library equips developers with cryptographic techniques and high-performance optimizations for secure computation. With the HESea Library, developers gain access to a wide range of functions that enable them to perform essential primitive, arithmetic, and logical operations on encrypted data.
What sets this library apart is its meticulous optimization, employing techniques such as ciphertext packing and batching to enhance efficiency and overall performance.
The Privasea API is a comprehensive set of protocols and tools built on top of the HESea Library. This API serves as a valuable resource for developers looking to construct privacy-preserving AI applications.
By leveraging the power of the underlying FHE schemes provided by the HESea Library, developers can create robust applications that prioritize data privacy and security. The Privasea API empowers developers to integrate advanced privacy-preserving features seamlessly into their AI applications.
Enabling secure computations on encrypted data is the task of Privanetix, an interconnected network of computation nodes. These nodes leverage FHE algorithms to perform calculations on encrypted data, ensuring that sensitive information remains hidden from the wrong eyes.
By distributing computations across multiple nodes, Privanetix empowers the scalability and efficiency of the Privasea AI Network. This network acts as a robust shield against data breaches and unauthorized access, further bolstering the security of users' sensitive information.
To efficiently manage the Privanetix network and motivate computation nodes, the Privasea Smart Contract Kit comes into play. This kit includes a series of smart contracts meticulously designed to handle various aspects of network management.
By using these smart contracts, organizations can effectively govern the Privanetix network, which makes sure everything goes smoothly. Moreover, the Privasea Smart Contract Kit provides incentives for computation nodes, encouraging their active participation and further strengthening the network's overall performance.
The Privasea AI Network is dedicated to addressing the crucial balance between user privacy and distributed computing resources during AI processing. It accomplishes this through a comprehensive approach, dividing Fully Homomorphic Encryption from theory to application into four distinct layers: Application Layer, Optimisation Layer, Arithmetic Layer, and Primitive Layer.
The network's generalized solution focuses on the bottom two layers of homomorphic application and revolves around the HESea library. Developed and open-sourced by Privasea, the HESea library enables users to encrypt their data or models using a Fully Homomorphic Encryption scheme.
Once encrypted, users can securely upload their sensitive data to the Privasea AI Network and delegate the calculation to the network. Leveraging the distributed computing resources available within the network, users can perform machine learning or other computations on their data while it remains in an encrypted state.
Privasea AI Network goes a step further with its customized solution, catering to the top two layers of homomorphic application: the Application Layer and Optimisation Layer. This tailored approach allows for more specific solutions that align with the unique requirements of each user.
Building upon the functionalities of the generalized solution, the customized solution introduces two vital features: efficiency and user-friendliness.
The Privasea AI Network strives to make its platform accessible to users without a background in cryptography or even programming. Users can easily navigate and leverage the network's capabilities, enabling them to perform privacy-preserving AI computations without the need for specialized expertise.