Privacy Explained: Concepts, Workflows and Tools
Stripping personally identifiable information before sending to a model. PII redaction detects names, emails, phone numbers, IDs and replaces them with tokens before the payload leaves your infrastructure. This guide pulls together everything on Onexial tagged privacy — 5 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Privacy
Before wiring anything together, the vocabulary has to be precise. These 2 definitions cover the terms that show up in almost every Privacy discussion — each one links to a full entry with an example and its own connections inside the graph.
PII Redaction
Stripping personally identifiable information before sending to a model.
Local LLM
A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
Workflows: how Privacy runs end to end
Concepts only matter once they become a repeatable process. Below are 1 documented workflows that apply Privacy to a concrete problem, with the steps, the tools involved and the variations worth testing.
The Privacy tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 1 stacks show which combinations hold up in production for Privacy, and what each layer is actually responsible for.
Trade-offs and comparisons
Most Privacy decisions are trade-offs rather than right answers. These 1 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Frequently asked questions
- What is PII Redaction?
- PII redaction detects names, emails, phone numbers, IDs and replaces them with tokens before the payload leaves your infrastructure. Reversed on the response for the end user.
- What is an example of PII Redaction?
- A customer-support pipeline replaces 'John (john@acme.com)' with '[USER_1] ([EMAIL_1])' pre-inference.
- Why does PII Redaction matter?
- Stripping personally identifiable information before sending to a model.
- What should I learn after PII Redaction?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
- What is Local LLM?
- Running an LLM locally means downloading open weights and serving them with a runtime such as Ollama, llama.cpp or vLLM. The benefits are data privacy, no per-token fees and offline use; the costs are hardware, maintenance and lower quality than the best hosted models. Quantized weights make 7–14B models usable on consumer GPUs and Apple Silicon.
- What is an example of Local LLM?
- A law firm summarises confidential contracts with a quantized 14B model on a workstation, so client documents are never sent to a third party.
- Why does Local LLM matter?
- A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
- What should I learn after Local LLM?
- Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.