Cognitive Data Privacy Platform

Privacy-Enhancing Technology to Protect, Share, and Comply Data.

Overview

Protecting Privacy, Empowering Data Sharing.

Xafe is a cognitive data privacy platform designed to protect sensitive enterprise data while enabling secure sharing and insightful data analysis. The platform leverages key privacy-enhancing technologies including differential privacy concepts such as privacy budget, sensitivity, and privacy loss to ensure that data sharing and querying do not compromise individual privacy.

  • 01Differential privacy at the core
  • 02Privacy budget · Sensitivity · Loss
  • 03Compliance-ready data sharing
Privacy vs Utility Trade-off Level Epsilon (Privacy Budget) 1.0 0.8 0.6 0.4 0.2 0.0 0 1 2 3 4 5 Privacy Utility
Privacy by Design

Privacy by Design
built into every
data operation.

Xafe employs precise control over privacy budget, ensuring sensitive information remains confidential while maximizing the utility of your data.

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Differentially Private Query Results
Obfuscation ε = 0.5 · noise σ = 1.2
Q: COUNT = 12,847
/ 01

Differentially Private Query Results

Xafe implements algorithms that add carefully calibrated noise to query results, ensuring that the output remains accurate for analytical purposes while protecting individual data entries.

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Anonymization k-anonymity · 32 records
Age (generalized 10-yr bins) ZIP (3-digit) k = 5 k = 6 k = 4 k = 4 All clusters k ≥ 4
/ 02

Anonymized Data Sharing

Xafe generates anonymized datasets by applying noise to raw data, making it difficult to re-identify individuals. This is particularly useful for sharing data with third parties or publishing datasets for public use.

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Privatization ε remaining: 0.38
CRITICAL ≤ 0.2 1.0 0.75 0.5 0.25 0.0 ε remaining Q₀ Q₁ Q₂ Q₃ Q₄ Q₅ Q₆ Query sequence −0.15 −0.12 −0.10 −0.10 −0.08 −0.07 ε budget ceiling = 1.0 REMAINING ε = 1.00
/ 03

Privacy Budget Management

Organizations can set and monitor the privacy budget using Xafe. Each query reduces the remaining budget, and Xafe provides tools to visualize and manage the trade-off between query accuracy and privacy.

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Generalization Privacy ↑ / Utility ↓ trade-off
1.0 0.75 0.5 0.25 0 0 1 2 3 4 5 ε (Privacy Budget) PRIV 0.62 UTIL 0.58 Privacy Utility
/ 04

Real-time Privacy and Utility Trade-offs

Xafe includes interactive tools that allow users to adjust privacy parameters and observe their impact on data utility in real-time. This helps in making informed decisions about the level of privacy protection needed for different datasets and queries.

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Why Xafe

The Future of Secure Data Sharing, balancing Privacy and Precision.

Xafe revolutionizes data privacy with its advanced privacy enhancing technologies including differential privacy algorithms, ensuring the highest standards of data protection. This compliance-ready solution fosters trust and empowers secure data sharing without compromising privacy.
Use Cases

Industry-specific cognitive privacy,
across every sector.

Check out industry-specific use cases and the importance of Cognitive Privacy in protecting sensitive data across various sectors, ensuring privacy and compliance.

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01 / 15 Finance Platform

Finance

By incorporating Differential Privacy into financial systems, institutions can analyze transaction patterns, detect anomalies, and assess risks without exposing individual customer data. This approach ensures compliance with strict regulatory requirements and builds customer trust, all while enabling innovative financial solutions such as personalized credit scoring and investment advice.

  • 01Fraud Detection
  • 02Customer Data Privacy
  • 03Risk Modeling
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Partnership

Let’s connect.

Partner with Xafe to unlock the future of privacy-enhanced data intelligence.

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