# Software/algorithm Side: Data Encryption > ⚠️ **Evaluation in Progress** > > Some of the mechanisms described on this page are currently **being evaluated and validated**. They represent design options under consideration, and may be refined, replaced, or omitted as the system evolves. In the digital age, where data security and privacy are critical, the need to protect user data within decentralized inference systems like Nesa cannot be overstated. As decentralized systems distribute computational tasks across various nodes, each potentially operated by different entities, the risk of exposing sensitive user data during the inference process intensifies. Therefore, ensuring user data's confidentiality and integrity becomes a cornerstone of trust and reliability in such systems. This section discusses our vision and implementation of homomorphic encryption and split learning to achieve this goal. --- # 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.nesa.ai/nesa/major-innovations/private-inference-for-ai/background-and-exploratory-notes/software-algorithm-side-data-encryption.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.