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DCH-36.6: Differential Privacy

DCH 1 — Low Protect

Mechanisms exist to implement differential privacy to prevent the disclosure of Personal Data (PD) by adding non-deterministic noise to analysis results using: (1) A defined privacy loss parameter; (2) Noise calibrated to query sensitivity; and (3) Accounting for cumulative privacy loss across queries.

Renumbered: Formerly DCH-23.6 in SCF 2025.4.

Control Question: Does the organization implement differential privacy to prevent the disclosure of Personal Data (PD) by adding non-deterministic noise to analysis results using: (1) A defined privacy loss parameter; (2) Noise calibrated to query sensitivity; and (3) Accounting for cumulative privacy loss across queries?

General (3)
Framework Mapping Values
NIST 800-53 R5.2 SI-19(06)
NIST 800-82 R3 SI-19(06)
NIST 800-160 Vol2 R1 SI-19(06)

Capability Maturity Model

Level 0 — Not Performed

Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.

Level 1 — Performed Informally

SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.

Level 2 — Planned & Tracked

SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 — Well Defined

Data Classification & Handling (DCH) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are well-documented and kept current by process owners. ▪ A Governance, Risk & Compliance (GRC) team, or similar function, is appropriately staffed and supported to implement and maintain DCH domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of data classification and handling operations (e.g., GRC platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to prevent disclosure of Personal Data (PD) by adding non-deterministic noise to the results of mathematical operations before the results are reported.

Level 4 — Quantitatively Controlled

Data Classification & Handling (DCH) capabilities, in addition to being standardized across the entity and centrally managed to ensure consistency across Technology Assets, Applications, Services and/or Data (TAASD), efforts are metrics driven to provide sufficient insight for decision makers to predict optimal performance, ensure continued operations and/or identify areas for improvement. Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Applicable SCR-CMM Level 3 (Well Defined) capabilities are implemented and operational. ▪ Metrics reporting includes quantitative analysis of Key Performance Indicators (KPIs). ▪ Metrics reporting includes quantitative analysis of Key Risk Indicators (KRIs). ▪ Scope of metrics, KPIs and KRIs covers organization-wide cybersecurity and data protection controls, including functions performed by third-parties. ▪ Organizational leadership maintains a formal process to objectively review and respond to metrics, KPIs and KRIs (e.g., monthly or quarterly review). ▪ Based on metrics analysis, process improvement recommendations are submitted for review and are handled in accordance with change control processes. ▪ Business and technical stakeholders are involved in reviewing and approving proposed changes to evolve capabilities.

Level 5 — Continuously Improving

Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.

Assessment Objectives

  1. DCH-36.6_A01 differential privacy is implemented to prevent the disclosure of Personal Data (PD) by adding non-deterministic noise to analysis results.
  2. DCH-36.6_A02 the privacy loss parameter used for differential privacy is defined.
  3. DCH-36.6_A03 the non-deterministic noise added to analysis results is calibrated to the sensitivity of the query.
  4. DCH-36.6_A04 cumulative privacy loss across queries is tracked and accounted for against the defined privacy loss parameter.

Technology Recommendations

Micro/Small

  • Data classification program
  • Data privacy program
  • Product / project management

Small

  • Data classification program
  • Data privacy program
  • Product / project management

Medium

  • Data classification program
  • Data privacy program
  • Product / project management

Large

  • Data classification program
  • Data privacy program
  • Product / project management

Enterprise

  • Data classification program
  • Data privacy program
  • Product / project management

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