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 Information Technology Laboratory /                  Applied Cybersecurity Division
      Privacy Engineering Program
    
      
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**NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management**ThePrivacy Frameworkis a voluntary tool intended to help organizations identify and manage privacy risk to build beneficial products and services while protecting individuals’ privacy.Visit the new Privacy FrameworkResource Repositoryto explore crosswalks, common Profiles, guidance, and tools to support implementation.Whether you’renew to the Frameworkor seeking more information aboutadoption, see ourhypothetical use case Profilesand updatedfrequently asked questions.Check out the companionroadmapto promote collaboration to address key privacy challenges for organizations and improve future versions of the Framework.**NIST Privacy Framework (PDF)****NIST Internal Report (NISTIR) 8062:*****An Introduction to Privacy Engineering and Risk Management in Federal Systems***NISTIR 8062 introduces the concept of applying systems engineering practices to privacy and provides a new model for conducting privacy risk assessments on federal systems.**NISTIR 8062 (PDF)****NIST Privacy Risk Assessment Methodology (PRAM)**The PRAM is a tool that applies the risk model from NISTIR 8062 and helps organizations analyze, assess, and prioritize privacy risks to determine how to respond and select appropriate solutions. The PRAM can help drive collaboration and communication between various components of an organization, including privacy, cybersecurity, business, and IT personnel.Worksheet 1: Framing Business Objectives and Organizational Privacy GovernanceWorksheet 2: Assessing System Design; Supporting Data MapWorksheet 3: Prioritizing RiskWorksheet 4: Selecting ControlsCatalog of Problematic Data Actions and Problems**PRAM (.ZIP)**   *Version: February 2019***NIST Special Publication (SP) 800-226:*****Guidelines for Evaluating Differential Privacy Guarantees***SP 800-226 is about*differential privacy*, a privacy-enhancing technology that quantifies privacy risk to individuals when their information appears in a dataset. In response to President Biden’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, SP 800-226 is intended to help agencies and practitioners of all backgrounds—policy makers, business owners, product managers, IT technicians, software engineers, data scientists, researchers, and academics—better understand how to evaluate promises made (and not made) when deploying differential privacy, including for privacy-preserving machine learning.Additionally, there is a supplemental package ofPython Jupyter notebooksthat illustrate how to achieve differential privacy and other concepts described in the publication.**SP 800-226**Used the PRAM and have suggestions for improving it? Head over to the Privacy Engineering Collaboration Space, where you can share feedback.**See the PRAM in the Collaboration Space**
  Privacy

    
    Created May 11, 2018, Updated January 26, 2024
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