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HR verified Verified Outcome TRL 9

Differential Privacy for Employee Analytics

domain Client: A major technology company handshake Provider: Internal / Viva schedule Deploy: Q1 2021 (Launch)
90 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

To analyze employee productivity and burnout without spying on individuals, the company applied Differential Privacy techniques to its 'Viva Insights' platform. This adds statistical noise to datasets, ensuring that managers can see aggregate trends (e.g., 'Marketing is working late') but cannot reverse-engineer the data to identify specific employees.

rate_review Analyst Verdict

"The only viable path for 'People Analytics'. Surveillance tools destroy trust and culture. Differential privacy enables the business value (workforce optimization) while mathematically guaranteeing individual anonymity, solving the privacy-utility trade-off."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

HR needed data on burnout, meeting overload, and collaboration patterns. However, analyzing email and calendar metadata raised massive privacy concerns. Employees feared 'Big Brother' monitoring and retaliation.

psychology The Solution

The system aggregates metadata (Outlook/Teams). It applies a differential privacy layer where noise is injected into the query results. Policy controls enforce minimum group sizes (e.g., no reports for teams <10 people) to prevent re-identification via elimination.

settings_suggest Technical & Deployment Specs

Integrations
Office 365
Deployment Model
SaaS
Data Classification
Employee Metadata
Estimated TCO / ROI
Low (Feature of suite)
POC Summary (2020-01-01 to 2021-02-01)

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Perception Medium Transparent communication; privacy dashboard for employees.
Utility Loss Low Noise level tuning.

trending_up Impact Trajectory

Audited value realization curve

Privacy guarantee via statistical noise Verified Outcome
Primary KPIMinimum cohort size enforcement (>10)
Audit CycleAdoption by Fortune 500 clients

policy Compliance & Gov

  • Standards: GDPR (Works Councils)
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Employee Metadata

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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The "Blind Verification" Protocol

How we verified these outcomes for A major technology company without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-881
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-881

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