# Typical Scenarios 2PM.Network harnesses the capabilities of Fully Homomorphic Encryption (FHE) to facilitate secure and private computation across various sectors Here are some typical scenarios where 2PM.Network’s FHE solutions are effectively applied: ### Trading Decision Model This scenario involves training models for predicting cryptocurrency price movements, ensuring privacy in users' decision-making processes. By using FHE, 2PM.Network allows financial traders and analysts to utilize sensitive financial data without exposing it, maintaining confidentiality while harnessing powerful predictive analytics. This setup is crucial for environments where investment decisions need to be data-driven and highly secure. ### Biometric Identification In this application, 2PM.Network safeguards users' biometric data while identifying individuals, adhering to strict privacy standards. Utilizing FHE, the platform ensures that biometric data, whether fingerprints, facial recognition, or other identifiers, are processed in an encrypted form. This prevents any possibility of privacy breaches while allowing for secure and reliable user authentication processes. ### Personalized Recommendation System This system delivers tailored recommendations for projects, products, or currencies, protecting users' behavioral data privacy. Through FHE, 2PM.Network ensures that user preferences and behaviors are analyzed without ever exposing the raw data, allowing companies to offer personalized experiences and suggestions based on encrypted data insights. This scenario is particularly beneficial for e-commerce and digital marketing platforms seeking to enhance user engagement without compromising privacy. ### LLM Joint Training 2PM.Network facilitates the collaborative training of a Large Language Model (LLM) using private data from multiple users, protecting their interaction privacy. This scenario supports sectors such as IP creations and collaborations. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.2pm.network/node-framework/fhe-machine-learning/typical-scenarios.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.