FAQ
How to participate as data contributors
To participate as data contributors in 2PM.Network, users have three primary pathways:
Using the Data Encryption Client: Contributors can utilize 2PM's Data Encryption Client to encrypt their data using Fully Homomorphic Encryption. This allows the data to remain encrypted even during processing, ensuring privacy and security. Once encrypted, the data is standardized and uploaded to 0G via an Index Contract. This method is particularly beneficial for those who wish to contribute data while maintaining the highest levels of privacy and security.
Participating in Federated Learning: Alternatively, contributors can act as node operators for Federated Learning. In this scenario, users provide their plaintext data in a standardized format, but the data is processed locally on their nodes. This means that the actual data does not leave the user's premises, maintaining privacy while still contributing to the collective training of AI models. This local computation helps in aggregating insights without compromising the data's confidentiality.
Engaging with Ecosystem Applications: Data contributors can also participate by engaging with various ecosystem applications provided by 2PM. During these interactions, they can contribute data directly through the applications, which are designed to respect user privacy and securely handle data contributions.
What's the difference between FHE and Federated Learning
Fully Homomorphic Encryption (FHE) and Federated Learning are two distinct technologies that enhance data privacy and security in different ways. Here’s how they differ:
1. Fully Homomorphic Encryption (FHE)
Purpose: FHE allows computations to be performed on encrypted data, returning encrypted results that, when decrypted, are the same as if the operations had been performed on the original data.
Key Benefit: The primary advantage of FHE is that it keeps data completely secure and unreadable to anyone without the decryption key, even while it's being processed. This is crucial in environments where data confidentiality is paramount, such as in financial services or healthcare.
Limitation: The main drawback of FHE is that it is computationally intensive and can significantly slow down data processing speeds, making it less practical for real-time or large-scale applications without substantial computational resources.
2. Federated Learning
Purpose: Federated Learning involves training machine learning models across multiple decentralized devices or servers holding local data samples, without exchanging the data itself. This means the data remains on the local device, and only model updates are shared to a central server.
Key Benefit: The significant benefit of Federated Learning is its ability to improve machine learning models with data from diverse sources while maintaining data privacy and reducing the risk of data exposure.
Limitation: A challenge with Federated Learning is that it requires careful coordination and robust network connections to handle the frequent updates between local and central servers. It also faces challenges in ensuring the quality and consistency of data across different nodes, which can affect model accuracy.
Comparison
Privacy vs. Practicality: FHE provides higher levels of data security as no meaningful data (not even in an encrypted form) is exposed during processing. Federated Learning, while also preserving privacy, allows for more practical and efficient use of data in training machine learning models since it doesn't require the heavy computational overhead of FHE.
Application Areas: FHE is more suited for situations where data cannot be exposed in any form, such as in sensitive communications or secure cloud computing. Federated Learning is more applicable in scenarios like mobile device usage and distributed networks, where data generated locally can be used to enhance products and services without compromising user privacy.
Why does 2PM.Network utilize both Federated Learning and Fully Homomorphic Encryption in its architecture
By combining FHE and Federated Learning, 2PM.Network can offer flexible, privacy-preserving computing solutions that are adaptable to the needs and capabilities of various users. This dual approach allows the network to maximize participation by accommodating different levels of data availability and computational capacity, thus enhancing the overall efficiency and effectiveness of the network's AI training and application deployment.
1. Integration of Fully Homomorphic Encryption (FHE)
Target Users: FHE is particularly beneficial for end-users who may have limited computational power or smaller datasets. These users benefit from FHE because it allows their data to be used in computations without revealing the actual data or requiring local processing.
Efficiency Considerations: Although FHE is known for its computational intensity and lower efficiency, it is crucial for scenarios where privacy cannot be compromised under any circumstances. For users with limited resources, the trade-off of slower processing times is often acceptable in exchange for the high level of security and privacy preservation that FHE provides.
Application: By integrating FHE, 2PM.Network ensures that even users who cannot participate in computationally heavy tasks can still contribute their data securely.
2. Integration of Federated Learning
Target Users: Federated Learning is ideally suited for users who own larger, standardized datasets and possess significant computational resources. This includes businesses and organizations that can handle more extensive data processing locally.
Efficiency Considerations: Federated Learning is much more efficient for these users because it leverages the collective power of multiple nodes (users) who can process data locally. The model updates, rather than the raw data, are shared across the network, significantly reducing the bandwidth and security concerns associated with transmitting sensitive data.
Application: For 2PM.Network, Federated Learning allows leveraging data from diverse sources efficiently. It enhances the AI models through a collaborative approach while maintaining data privacy.
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